In [109]:
plot_dataframe_heatmap(dataset, datatype)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from umap import UMAP
from sklearn.cluster import HDBSCAN
from sklearn.decomposition import PCA
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import math
import os
import cv2
import imageio
from IPython.display import display, Image
from tqdm import tqdm
import ipywidgets as widgets
from IPython.display import display
import joblib
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import holoviews as hv
from holoviews import opts, dim
from statesegmentation import GSBS
from matplotlib import patches
%matplotlib inline
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from scipy.stats import zscore
from scipy.spatial import ConvexHull
from scipy.spatial.distance import cdist
from sklearn.metrics import silhouette_score
from sklearn.model_selection import ParameterGrid
import warnings
from sklearn.exceptions import ConvergenceWarning
from sklearn.metrics import mean_squared_error
from sklearn.metrics import pairwise_distances
from matplotlib.colors import to_hex
from bokeh.plotting import show
import networkx as nx
def transform_mousecentric(df, main_dir, mouse, freezing_frame_dict):
'''
Transforms data into mouse-centric version.
Parameters:
df (pd.DataFrame): The input data.
main_dir (str): The main directory containing transformation results.
mouse (str): Mouse identifier.
freezing_frame_dict (dict): Dictionary of freezing frame labels for each mouse.
Returns:
pd.DataFrame: Transformed data in mouse-centric version.
'''
folder_path = main_dir + "/Datasets/MouseCentric"
file_path = f'/{mouse}_MouseCentric_data.csv'
if not os.path.exists(folder_path + file_path):
mc_df = df.copy()
mc_df = mc_df.iloc[:, 1:-2]
mc_df = mc_df.astype(float)
s1_x = mc_df.iloc[:,39].copy()
s1_y = mc_df.iloc[:,40].copy()
coordinates_columns = [col for col in mc_df.columns if 'x' in col or 'y' in col]
for col_x, col_y in zip(coordinates_columns[::2], coordinates_columns[1::2]):
x = mc_df[col_x]
y = mc_df[col_y]
transformed_x = x - s1_x
transformed_y = y - s1_y
mc_df[col_x] = transformed_x
mc_df[col_y] = transformed_y
mc_df.iloc[:,39]=s1_x
mc_df.iloc[:,40]=s1_y
mc_df.insert(0, 'frame_number', df['frame_number'])
mc_df['mouse_no'] = float(mouse)
mc_df['freezing_label'] = freezing_frame_dict[mouse]
if not os.path.exists(folder_path):
os.makedirs(folder_path)
mc_df.to_csv(folder_path + file_path, index=False)
else:
mc_df = pd.read_csv(folder_path + file_path)
return mc_df
def transform_changecoord(df, main_dir, mouse, freezing_frame_dict):
'''
Transforms data into change coordinate version.
Parameters:
df (pd.DataFrame): The input data.
main_dir (str): The main directory containing transformation results.
mouse (str): Mouse identifier.
freezing_frame_dict (dict): Dictionary of freezing frame labels for each mouse.
Returns:
pd.DataFrame: Transformed data in change coordinate version.
'''
folder_path = main_dir + "/Datasets/ChangeCoords"
file_path = f'/{mouse}_ChangeCoords_data.csv'
if not os.path.exists(folder_path + file_path):
cc_df = df.copy()
cc_df = cc_df.iloc[:, 1:-2]
cc_df = cc_df.astype(float)
coordinates_columns = [col for col in cc_df.columns if 'x' in col or 'y' in col]
for col_x, col_y in zip(coordinates_columns[::2], coordinates_columns[1::2]):
cc_df[col_x] = cc_df[col_x] - cc_df[col_x].shift(1)
cc_df[col_y] = cc_df[col_y] - cc_df[col_y].shift(1)
cc_df[col_x] = cc_df[col_x].fillna(0)
cc_df[col_y] = cc_df[col_y].fillna(0)
cc_df.insert(0, 'frame_number', df['frame_number'])
cc_df['mouse_no'] = float(mouse)
cc_df['freezing_label'] = freezing_frame_dict[mouse]
if not os.path.exists(folder_path):
os.makedirs(folder_path)
cc_df.to_csv(folder_path + file_path, index=False)
else:
cc_df = pd.read_csv(folder_path + file_path)
return cc_df
def transform_mcpolar(mc_df, main_dir, mouse, freezing_frame_dict):
'''
Transforms data into mouse-centric polar coordinates version.
Parameters:
mc_df (pd.DataFrame): The input data in mouse-centric coordinates.
main_dir (str): The main directory containing transformation results.
mouse (str): Mouse identifier.
freezing_frame_dict (dict): Dictionary of freezing frame labels for each mouse.
Returns:
pd.DataFrame: Transformed data in mouse-centric polar coordinates.
'''
folder_path = main_dir + "/Datasets/MouseCentricPolar"
file_path = f'/{mouse}_MouseCentricPolar_data.csv'
if not os.path.exists(folder_path + file_path):
mcp_df = mc_df.copy()
mcp_df = mcp_df.iloc[:,1:-2]
mcp_df = mcp_df.astype(float)
coordinates_columns = [col for col in mcp_df.columns if 'x' in col or 'y' in col]
for col_x, col_y in zip(coordinates_columns[::2], coordinates_columns[1::2]):
x = mcp_df[col_x]
y = mcp_df[col_y]
r = np.sqrt(x**2 + y**2)
theta = np.arctan2(y, x)
mcp_df[col_x] = r
mcp_df[col_y] = theta
mcp_df.insert(0, 'frame_number', mc_df['frame_number'])
mcp_df['mouse_no'] = float(mouse)
mcp_df['freezing_label'] = freezing_frame_dict[mouse]
if not os.path.exists(folder_path):
os.makedirs(folder_path)
mcp_df.to_csv(folder_path + file_path, index=False)
else:
mcp_df = pd.read_csv(folder_path + file_path)
return mcp_df
def transform_mcdirectional(mc_df, mcp_df, main_dir, mouse, freezing_frame_dict):
'''
Transforms data into mouse-centric directional coordinates version.
Parameters:
mc_df (pd.DataFrame): The input data in mouse-centric coordinates.
mcp_df (pd.DataFrame): The data in mouse-centric polar coordinates for reference.
main_dir (str): The main directory containing transformation results.
mouse (str): Mouse identifier.
freezing_frame_dict (dict): Dictionary of freezing frame labels for each mouse.
Returns:
pd.DataFrame: Transformed data in mouse-centric directional coordinates.
'''
folder_path = main_dir + "/Datasets/MouseDirectional"
file_path = f'/{mouse}_MouseDirectional_data.csv'
if not os.path.exists(folder_path + file_path):
md_df = mc_df.copy()
md_df = md_df.iloc[:,1:-2]
md_df = md_df.astype(float)
coordinates_columns = [col for col in md_df.columns if 'x' in col or 'y' in col]
for frame_number, frame_data_polar in mcp_df.groupby('frame_number'):
rotation_angle = frame_data_polar.at[frame_data_polar.index[0], "('S2', 'y')"]
affine_matrix = np.array([[np.cos(rotation_angle), -np.sin(rotation_angle)],
[np.sin(rotation_angle), np.cos(rotation_angle)]])
for col_x, col_y in zip(coordinates_columns[::2], coordinates_columns[1::2]):
x = md_df[col_x]
y = md_df[col_y]
points = np.column_stack((x, y))
transformed_points = np.dot(points[frame_number], affine_matrix)
md_df.at[frame_number, col_x] = transformed_points[0]
md_df.at[frame_number, col_y] = transformed_points[1]
md_df.insert(0, 'frame_number', mc_df['frame_number'])
md_df['mouse_no'] = float(mouse)
md_df['freezing_label'] = freezing_frame_dict[mouse]
if not os.path.exists(folder_path):
os.makedirs(folder_path)
md_df.to_csv(folder_path + file_path, index=False)
else:
md_df = pd.read_csv(folder_path + file_path)
return md_df
def transform_mcdirectionalpolar(md_df, main_dir, mouse, freezing_frame_dict):
'''
Transforms data into mouse-centric directional polar coordinates version.
Parameters:
md_df (pd.DataFrame): The input data in mouse-centric directional coordinates.
main_dir (str): The main directory containing transformation results.
mouse (str): Mouse identifier.
freezing_frame_dict (dict): Dictionary of freezing frame labels for each mouse.
Returns:
pd.DataFrame: Transformed data in mouse-centric directional polar coordinates.
'''
folder_path = main_dir + "/Datasets/MouseDirectionalPolar"
file_path = f'/{mouse}_MouseDirectionalPolar_data.csv'
if not os.path.exists(folder_path + file_path):
mdp_df = md_df.copy()
mdp_df = mdp_df.iloc[:,1:-2]
mdp_df = mdp_df.astype(float)
coordinates_columns = [col for col in md_df.columns if 'x' in col or 'y' in col]
for col_x, col_y in zip(coordinates_columns[::2], coordinates_columns[1::2]):
x = mdp_df[col_x]
y = mdp_df[col_y]
r = np.sqrt(x**2, y**2)
theta = np.arctan2(y, x)
mdp_df[col_x] = r
mdp_df[col_y] = theta
mdp_df.insert(0, 'frame_number', md_df['frame_number'])
mdp_df['mouse_no'] = float(mouse)
mdp_df['freezing_label'] = freezing_frame_dict[mouse]
if not os.path.exists(folder_path):
os.makedirs(folder_path)
mdp_df.to_csv(folder_path + file_path, index=False)
else:
mdp_df = pd.read_csv(folder_path + file_path)
return mdp_df
def create_dataset(main_dir: str, datatype: str):
'''
Create data files with the given transformation type from the original data in the given main directory.
Parameters:
main_dir (str): The main directory containing the original data and transformation results.
datatype (str): The type of data transformation to apply.
Returns:
pd.DataFrame: The dataset created based on the specified data transformation.
Supported 'datatype' values:
- "Original": Original data.
- "MouseCentric": Mouse-centric data.
- "MouseCentricPolar": Mouse-centric polar data.
- "ChangeCoord": Data with changed coordinates.
- "MouseDirectional": Mouse-centric directional data.
- "MouseDirectionalPolar": Mouse-centric directional polar data.
'''
folder_path = main_dir + "/Datasets/Original"
if not os.path.exists(folder_path):
os.makedirs(folder_path)
dataframes = []
freezing_frame_dict = pd.read_pickle(main_dir + "/Datasets/Freezing_predictions_dict.pkl")
filtered_dir = os.path.join(main_dir, 'filtered')
files = os.listdir(filtered_dir)
for file in tqdm(files, desc="Processing Files"):
mouse=file.split("_")[1]+"."+file.split("_")[2][0:-3]
file=os.path.join(main_dir+'/filtered/', file)
new_file_path = f'/{mouse}_Original_data.csv'
if not os.path.exists(folder_path + new_file_path):
df = pd.read_csv(file,header=[1,2])
df.columns = ['frame_number'] + list(df.columns[1:])
df['mouse_no'] = float(mouse)
df['freezing_label'] = freezing_frame_dict[mouse]
df.to_csv(folder_path + new_file_path, index=False)
else:
df = pd.read_csv(folder_path + new_file_path)
if datatype == "Original":
dataframes.append(df)
elif datatype == "MouseCentric":
mc_df = transform_mousecentric(df, main_dir, mouse, freezing_frame_dict)
dataframes.append(mc_df)
elif datatype == "MouseCentricPolar":
mc_df = transform_mousecentric(df, main_dir, mouse, freezing_frame_dict)
mcp_df = transform_mcpolar(mc_df, main_dir, mouse, freezing_frame_dict)
dataframes.append(mcp_df)
elif datatype == "ChangeCoord":
cc_df = transform_changecoord(df, main_dir, mouse, freezing_frame_dict)
dataframes.append(cc_df)
elif datatype == "Features":
feat_df = feature_selection(df, main_dir, mouse, freezing_frame_dict)
dataframes.append(feat_df)
elif datatype == "MouseDirectional":
mc_df = transform_mousecentric(df, main_dir, mouse, freezing_frame_dict)
mcp_df = transform_mcpolar(mc_df, main_dir, mouse, freezing_frame_dict)
mcd_df = transform_mcdirectional(mc_df, mcp_df, main_dir, mouse, freezing_frame_dict)
dataframes.append(mcd_df)
elif datatype == "MouseDirectionalPolar":
mc_df = transform_mousecentric(df, main_dir, mouse, freezing_frame_dict)
mcp_df = transform_mcpolar(mc_df, main_dir, mouse, freezing_frame_dict)
md_df = transform_mcdirectional(mc_df, mcp_df, main_dir, mouse, freezing_frame_dict)
mdp_df = transform_mcdirectionalpolar(md_df, main_dir, mouse, freezing_frame_dict)
dataframes.append(mdp_df)
dataset = pd.concat(dataframes, ignore_index=True)
drop_cols = [col for col in dataset.columns if 'likelihood' in col]
dataset = dataset.drop(drop_cols, axis=1)
return dataset
def angle3pt(ax, ay, bx, by, cx, cy):
ang = math.degrees(
math.atan2(cy - by, cx - bx) - math.atan2(ay - by, ax - bx))
return ang + 360 if ang < 0 else ang
def feature_selection(df, main_dir, mouse, freezing_frame_dict):
'''
Extracts features from data to use as dataset
Parameters:
df (pd.DataFrame): The input data.
main_dir (str): The main directory containing transformation results.
mouse (str): Mouse identifier.
freezing_frame_dict (dict): Dictionary of freezing frame labels for each mouse.
Returns:
pd.DataFrame: Dataset containing (only) extracted features
'''
folder_path = main_dir + "/Datasets/Features"
file_path = f'/{mouse}_Features_data.csv'
if not os.path.exists(folder_path + file_path):
df_copy = df.copy()
df_copy = df_copy.iloc[:, 1:-2]
df_copy = df_copy.astype(float)
feat_df = pd.DataFrame()
columnHeaders = ["Mouse_nose_x", "Mouse_nose_y", "Mouse_nose_p", \
"Mouse_H1R_x", "Mouse_H1R_y", "Mouse_H1R_p", \
"Mouse_H2R_x", "Mouse_H2R_y", "Mouse_H2R_p", \
"Mouse_H1L_x", "Mouse_H1L_y", "Mouse_H1L_p", \
"Mouse_H2L_x", "Mouse_H2L_y", "Mouse_H2L_p", \
"Mouse_B1R_x", "Mouse_B1R_y", "Mouse_B1R_p", \
"Mouse_B2R_x", "Mouse_B2R_y", "Mouse_B2R_p", \
"Mouse_B3R_x", "Mouse_B3R_y", "Mouse_B3R_p", \
"Mouse_B1L_x", "Mouse_B1L_y", "Mouse_B1L_p", \
"Mouse_B2L_x", "Mouse_B2L_y", "Mouse_B2L_p", \
"Mouse_B3L_x", "Mouse_B3L_y", "Mouse_B3L_p", \
"Mouse_tail_x", "Mouse_tail_y", "Mouse_tail_p", \
"Mouse_S2_x", "Mouse_S2_y", "Mouse_S2_p", \
"Mouse_S1_x", "Mouse_S1_y", "Mouse_S1_p"]
df_copy.columns = columnHeaders
df_copy['Mouse_poly_area'] = df_copy.apply(lambda x: ConvexHull(np.array(
[[x['Mouse_nose_x'], x["Mouse_nose_y"]],
[x['Mouse_H1R_x'], x["Mouse_H1R_y"]],
[x['Mouse_H2R_x'], x["Mouse_H2R_y"]],
[x['Mouse_H1L_x'], x["Mouse_H1L_y"]],
[x['Mouse_H2L_x'], x["Mouse_H2L_y"]],
[x['Mouse_B1R_x'], x["Mouse_B1R_y"]],
[x['Mouse_B2R_x'], x["Mouse_B2R_y"]],
[x['Mouse_B3R_x'], x["Mouse_B3R_y"]],
[x['Mouse_B1L_x'], x["Mouse_B1L_y"]],
[x['Mouse_B2L_x'], x["Mouse_B2L_y"]],
[x['Mouse_B3L_x'], x["Mouse_B3L_y"]],
[x['Mouse_tail_x'], x["Mouse_tail_y"]],
[x['Mouse_S2_x'], x["Mouse_S2_y"]],
[x['Mouse_S1_x'], x["Mouse_S1_y"]]])).area, axis=1)
feat_df['Mouse_poly_area'] = df_copy['Mouse_poly_area'].copy()
df_copy_shifted = df_copy.shift(periods=1)
df_copy['Movement_nose'] = np.sqrt((df_copy_shifted.Mouse_nose_x - df_copy.Mouse_nose_x) ** 2 + ((df_copy_shifted.Mouse_nose_y - df_copy.Mouse_nose_y) ** 2))
df_copy['Movement_tail'] = np.sqrt((df_copy_shifted.Mouse_tail_x - df_copy.Mouse_tail_x) ** 2 + ((df_copy_shifted.Mouse_tail_y - df_copy.Mouse_tail_y) ** 2))
df_copy['Movement_H1L'] = np.sqrt((df_copy_shifted.Mouse_H1L_x - df_copy.Mouse_H1L_x) ** 2 + (df_copy_shifted.Mouse_H1L_y - df_copy.Mouse_H1L_y) ** 2)
df_copy['Movement_H1R'] = np.sqrt((df_copy_shifted.Mouse_H1R_x - df_copy.Mouse_H1R_x) ** 2 + (df_copy_shifted.Mouse_H1R_y - df_copy.Mouse_H1R_y) ** 2)
df_copy['Movement_H2L'] = np.sqrt((df_copy_shifted.Mouse_H2L_x - df_copy.Mouse_H2L_x) ** 2 + (df_copy_shifted.Mouse_H2L_y - df_copy.Mouse_H2L_y) ** 2)
df_copy['Movement_H2R'] = np.sqrt((df_copy_shifted.Mouse_H2R_x - df_copy.Mouse_H2R_x) ** 2 + (df_copy_shifted.Mouse_H2R_y - df_copy.Mouse_H2R_y) ** 2)
df_copy['Movement_B1L'] = np.sqrt((df_copy_shifted.Mouse_B1L_x - df_copy.Mouse_B1L_x) ** 2 + (df_copy_shifted.Mouse_B1L_y - df_copy.Mouse_B1L_y) ** 2)
df_copy['Movement_B1R'] = np.sqrt((df_copy_shifted.Mouse_B1R_x - df_copy.Mouse_B1R_x) ** 2 + (df_copy_shifted.Mouse_B1R_y - df_copy.Mouse_B1R_y) ** 2)
df_copy['Movement_B2L'] = np.sqrt((df_copy_shifted.Mouse_B2L_x - df_copy.Mouse_B2L_x) ** 2 + (df_copy_shifted.Mouse_B2L_y - df_copy.Mouse_B2L_y) ** 2)
df_copy['Movement_B2R'] = np.sqrt((df_copy_shifted.Mouse_B2R_x - df_copy.Mouse_B2R_x) ** 2 + (df_copy_shifted.Mouse_B2R_y - df_copy.Mouse_B2R_y) ** 2)
df_copy['Movement_B3L'] = np.sqrt((df_copy_shifted.Mouse_B3L_x - df_copy.Mouse_B3L_x) ** 2 + (df_copy_shifted.Mouse_B3L_y - df_copy.Mouse_B3L_y) ** 2)
df_copy['Movement_B3R'] = np.sqrt((df_copy_shifted.Mouse_B3R_x - df_copy.Mouse_B3R_x) ** 2 + (df_copy_shifted.Mouse_B3R_y - df_copy.Mouse_B3R_y) ** 2)
df_copy['Movement_S1'] = np.sqrt((df_copy_shifted.Mouse_S1_x - df_copy.Mouse_S1_x) ** 2 + ((df_copy_shifted.Mouse_S1_y - df_copy.Mouse_S1_y) ** 2))
df_copy['Movement_S2'] = np.sqrt((df_copy_shifted.Mouse_S2_x - df_copy.Mouse_S2_x) ** 2 + ((df_copy_shifted.Mouse_S2_y - df_copy.Mouse_S2_y) ** 2))
feat_df['Mouse_polygon_size_change'] = df_copy_shifted.Mouse_poly_area - df_copy.Mouse_poly_area
movement_columns = [
'Movement_nose',
'Movement_tail',
'Movement_H1L',
'Movement_H1R',
'Movement_H2L',
'Movement_H2R',
'Movement_B1L',
'Movement_B1R',
'Movement_B2L',
'Movement_B2R',
'Movement_B3L',
'Movement_B3R',
'Movement_S1',
'Movement_S2']
feat_df['Movement_S2'] = df_copy['Movement_S2'].copy()
feat_df['Total_movement_all_bodyparts'] = df_copy[movement_columns].sum(axis=1)
feat_df['Mouse_angle'] = df_copy.apply(lambda x: angle3pt(x['Mouse_nose_x'], x['Mouse_nose_y'], x['Mouse_S2_x'], x['Mouse_S2_y'], x['Mouse_tail_x'], x['Mouse_tail_y']), axis=1)
large_euclidean_list = []
mean_euclidean_list = []
for index, row in df_copy.iterrows():
body_parts_np_array = np.array([
[row['Mouse_nose_x'], row['Mouse_nose_y']],
[row['Mouse_H1R_x'], row['Mouse_H1R_y']],
[row['Mouse_H2R_x'], row['Mouse_H2R_y']],
[row['Mouse_H1L_x'], row['Mouse_H1L_y']],
[row['Mouse_H2L_x'], row['Mouse_H2L_y']],
[row['Mouse_B1R_x'], row['Mouse_B1R_y']],
[row['Mouse_B2R_x'], row['Mouse_B2R_y']],
[row['Mouse_B3R_x'], row['Mouse_B3R_y']],
[row['Mouse_B1L_x'], row['Mouse_B1L_y']],
[row['Mouse_B2L_x'], row['Mouse_B2L_y']],
[row['Mouse_B3L_x'], row['Mouse_B3L_y']],
[row['Mouse_tail_x'], row['Mouse_tail_y']],
[row['Mouse_S2_x'], row['Mouse_S2_y']],
[row['Mouse_S1_x'], row['Mouse_S1_y']]]).astype(int)
dist_euclidean = cdist(body_parts_np_array, body_parts_np_array, metric='euclidean')
large_euclidean = np.amax(dist_euclidean)
mean_euclidean = np.mean(dist_euclidean)
large_euclidean_list.append(large_euclidean)
mean_euclidean_list.append(mean_euclidean)
feat_df['Largest_euclidean_distance_hull'] = large_euclidean_list
feat_df['Mean_euclidean_distance_hull'] = mean_euclidean_list
feat_df['Total_movement_all_bodyparts_mean_25'] = feat_df['Total_movement_all_bodyparts'].rolling(25, min_periods=1).mean()
feat_df['Mean_euclidean_distance_hull_mean_25'] = feat_df['Mean_euclidean_distance_hull'].rolling(25, min_periods=1).mean()
feat_df['Largest_euclidean_distance_hull_mean_25'] = feat_df['Largest_euclidean_distance_hull'].rolling(25, min_periods=1).mean()
feat_df['Movement_S2_mean_25'] = feat_df['Movement_S2'].rolling(25, min_periods=1).mean()
feat_df['Total_movement_all_bodyparts_median_25'] = feat_df['Total_movement_all_bodyparts'].rolling(25, min_periods=1).median()
feat_df['Mean_euclidean_distance_hull_median_25'] = feat_df['Mean_euclidean_distance_hull'].rolling(25, min_periods=1).median()
feat_df['Largest_euclidean_distance_hull_median_25'] = feat_df['Largest_euclidean_distance_hull'].rolling(25, min_periods=1).median()
feat_df['Movement_S2_median_25'] = feat_df['Movement_S2'].rolling(25, min_periods=1).median()
feat_df['Total_movement_all_bodyparts_both_deviation'] = (feat_df['Total_movement_all_bodyparts'].mean() - feat_df['Total_movement_all_bodyparts'])
feat_df['Largest_euclid_distances_hull_deviation'] = (feat_df['Largest_euclidean_distance_hull'].mean() - feat_df['Largest_euclidean_distance_hull'])
feat_df['Mean_euclid_distances_hull_deviation'] = (feat_df['Mean_euclidean_distance_hull'].mean() - feat_df['Mean_euclidean_distance_hull'])
feat_df['Movement_deviation_S2'] = (feat_df['Movement_S2'].mean() - feat_df['Movement_S2'])
feat_df['Polygon_deviation'] = (feat_df['Mouse_poly_area'].mean() - feat_df['Mouse_poly_area'])
feat_df['Movement_percentile_rank'] = feat_df['Movement_S2'].rank(pct=True)
feat_df['Total_movement_all_bodyparts_percentile_rank'] = feat_df['Total_movement_all_bodyparts'].rank(pct=True)
feat_df['Largest_euclid_distances_hull_percentile_rank'] = feat_df['Largest_euclidean_distance_hull'].rank(pct=True)
feat_df['Mean_euclid_distances_hull_percentile_rank'] = feat_df['Mean_euclidean_distance_hull'].rank(pct=True)
feat_df['Polygon_percentile_rank'] = feat_df['Mouse_poly_area'].rank(pct=True)
feat_df = feat_df.fillna(0)
feat_df.insert(0, 'frame_number', df['frame_number'])
feat_df['mouse_no'] = float(mouse)
feat_df['freezing_label'] = freezing_frame_dict[mouse]
if not os.path.exists(folder_path):
os.makedirs(folder_path)
feat_df.to_csv(folder_path + file_path, index=False)
else:
feat_df = pd.read_csv(folder_path + file_path)
return feat_df
def generate_sub_dataset(dataset, mouse_number=None, frames_per_video=11250):
"""
Generate a sub-dataset by selecting random videos or videos from a specific mouse
from the given dataset.
Args:
dataset (pd.DataFrame): The original dataset.
num_videos (int or None): The number of random videos to select. Set to None if specifying a mouse.
frames_per_video (int): The number of frames per video.
mouse_number (int or None): The mouse number to select videos for. Set to None for random selection.
Returns:
pd.DataFrame: A sub-dataset containing the selected videos.
"""
if mouse_number is not None:
# Select videos for a specific mouse
sub_dataset = dataset[dataset['mouse_no'] == mouse_number]
print(f'The data for mouse {mouse_number} was extracted.')
return sub_dataset.reset_index(drop=True)
else:
# Generate random video indices
selected_video_indices = np.random.choice(range(1, 99), size=1, replace=False)
selected_videos = []
# Create slices for each selected video
for video_index in selected_video_indices:
# Calculate the starting and ending frame indices for the video
start_frame = video_index * frames_per_video
end_frame = (video_index + 1) * frames_per_video
# Use .loc to create a slice by row index
video_slice = dataset.loc[start_frame:end_frame - 1]
selected_videos.append(video_slice)
# Concatenate the selected videos to create a sub-dataset
sub_dataset = pd.concat(selected_videos, ignore_index=True)
print("The following mouse was randomly selected: "+ " ".join(map(str, sub_dataset['mouse_no'].unique())))
return sub_dataset
def get_dataset(main_folder, datatype, mouse_number=None):
"""
Retrieve a subset of a dataset based on the specified parameters.
Parameters:
- main_folder (str): The main folder containing the dataset.
- datatype (str): The type of data to retrieve from the dataset.
- mouse_number (int or None, optional): The specific mouse number to filter the dataset.
If None, the function will retrieve the entire dataset without any mouse number filtering.
Returns:
- subset (pd.DataFrame): A subset of the dataset based on the specified parameters.
"""
dataset = create_dataset(main_folder, datatype)
subset = generate_sub_dataset(dataset, mouse_number)
print(f"The shape of the data: {subset.shape[0]} frames by {subset.shape[-1]} columns of which three are identifying columns: \n'frame_number', 'mouse_no', and 'freezing_label'. The rest are features.")
print("The mouse data has a video rate of 25 frames per second.")
return subset
def plot_dataframe_heatmap(df, datatype):
"""
Plot a DataFrame as a heatmap with features on the y-axis and time frames on the x-axis.
Parameters:
- df (pd.DataFrame): The input DataFrame with features as columns and time frames as rows.
Returns:
- None (displays the heatmap plot).
"""
drop_cols = ['frame_number', 'mouse_no', 'freezing_label']
# Drop non-feature columns
df = df.drop(drop_cols, axis=1)
# Get the list of columns and sort them
cols = df.columns
if datatype != 'Features':
# Group columns by x and y and concatenate them
columns_reordered = [col for col in cols if 'x' in col] + [col for col in cols if 'y' in col]
# Create a new DataFrame with reordered columns
df_reordered = df[columns_reordered]
else:
df_reordered = df.copy()
# Transpose the DataFrame
df_transposed = df_reordered.transpose()
# Apply z-score normalization to each feature
normalized_data_transposed = zscore(df_transposed, axis=1)
# Set the style for seaborn
sns.set()
# Create a figure with two subplots
fig, axs = plt.subplots(1, 2, figsize=(16, 6))
# Plot the first heatmap
sns.heatmap(df_transposed, cmap='viridis', cbar_kws={'label': 'Feature Values'}, ax=axs[0])
axs[0].set_xlabel('Time Frames')
axs[0].set_ylabel('Features')
axs[0].set_title('Features Per Time Frame')
# Plot the second heatmap
sns.heatmap(normalized_data_transposed, cmap='viridis', cbar_kws={'label': 'Normalized Feature Values'}, ax=axs[1])
axs[1].set_xlabel('Time Frames')
axs[1].set_ylabel('Features')
axs[1].set_title('Normalized Features Per Time Frame')
# Adjust layout to prevent overlapping
plt.tight_layout()
# Show the plot
plt.show()
sns.reset_defaults()
def fit_pca_and_hdbscan(dataset, min_clusters=10, max_clusters=40):
'''
Finds an optimal HDBSCAN fit on the data whilst keeping a limit on the amount of clusters
that are obtained with HDBSCAN.
Parameters:
dataset (pd.DataFrame): The data to apply PCA and clustering on.
min_clusters (int): Minimum amount of clusters constraint on clustering.
max_clusters (int): Maximum amount of clusters constraint on clustering.
Returns:
best_pca_embedding: The PCA result that gave the best silhouette score.
best_cluster_labels: The cluster labels that gave the ebst silhouette score.
'''
hdbscan_param_grid = {'min_cluster_size': np.arange(5, 40, 5),
'min_samples': np.arange(5, 40, 5)}
# Generate all possible combinations of hyperparameter values for HDBSCAN
hdbscan_grid = ParameterGrid(hdbscan_param_grid)
best_score = -1
best_hdbscan_params = None
# Turn off userwarnings for cleaner outputs
warnings.filterwarnings("ignore", category=UserWarning)
# Grid search loop
for hdbscan_params in tqdm(hdbscan_grid, total=len(list(hdbscan_grid)), desc="HDBSCAN Parameter Grid Search"):
pca = PCA(n_components=3)
pca_result = pca.fit_transform(dataset.iloc[:, 1:-2])
# Perform HDBSCAN clustering
cluster_labels = HDBSCAN(**hdbscan_params).fit_predict(pca_result)
# Check the number of clusters and apply constraints
num_clusters = len(np.unique(cluster_labels))
# Update best parameters if the amount of clusters is within the set limits
if num_clusters <= max_clusters and num_clusters >= min_clusters:
# Calculate silhouette score from HDBSCAN clustering
silhouette = silhouette_score(pca_result, cluster_labels)
if silhouette > best_score:
# Calculate reconstruction error
reconstructed_data = pca.inverse_transform(pca_result)
error = mean_squared_error(dataset.iloc[:, 1:-2], reconstructed_data)
# Calculate preservation of distances: trustworthiness and continuity
# original_distances = pairwise_distances(dataset.iloc[:, 1:-2], metric='euclidean')
# trustworthiness_measure = trustworthiness(original_distances, umap_embedding, metric='euclidean')
# continuity_measure = continuity(original_distances, umap_embedding, metric='euclidean')
# Update best parameters if the current iteration improves the score
best_score = silhouette
best_hdbscan_params = hdbscan_params
best_pca_embedding = pca_result
best_cluster_labels = cluster_labels
# Reset userwarnings back to default
warnings.resetwarnings()
if best_hdbscan_params is None:
print("Sorry no clustering parameters were found for your cluster limits. You can try lowering or raising your cluster amount limits.")
else:
print("Best HDBSCAN Parameters:", best_hdbscan_params)
print(f"\nReconstruction error: {error}")
# print(f"Preservation of distances: Trustworthiness = {trustworthiness_measure}, continuity = {continuity_measure}")
print(f"Silhouette score: {best_score}")
print("\nAmount of clusters:", len(np.unique(best_cluster_labels)))
return best_pca_embedding, best_cluster_labels
def plot_pca_and_hdbscan_results(pca_embedding, cluster_labels, dataset):
'''
Plots various results for both PCA and HDBSCAN clustering.
Parameters:
pca_embedding: Predicted PCA values.
cluster_labels: HDBSCAN cluster labels.
dataset (pd.DataFrame): Frame dataset.
Returns:
None
'''
# Plot PCA
print("These plots show the data spatially reduced using PCA:")
visualize_pca(pca_embedding)
# Plot HDBSCAN
print("These plots show the same PCA data but clustered using HDBSCAN:")
color_mapping = visualize_hdbscan(pca_embedding, cluster_labels)
# Plot barcodes
print("The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. \nThe barcodes also show the freezing frames using black markers.")
print("The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). \n- FRC is the percentage of frames within a cluster that are freezing frames. \n- CFC is the percentage of total freezing frames that belong to a specific cluster.")
data = dataset[:len(cluster_labels)].copy()
plot_combined_timeline_for_clusters(data, cluster_labels, color_mapping)
def visualize_pca(pca_embedding):
'''
Plots the given PCA embedding in a 2D plot if the embedding has 2 components
and in both 2D and 3D plots if the embedding has 3 components.
Parameters:
umap_embedding: Predicted PCA values.
Returns:
None
'''
if pca_embedding.shape[1] == 2:
# 2D plot
plt.figure(figsize=(8, 6))
plt.scatter(pca_embedding[:, 0], pca_embedding[:, 1], alpha=0.1)
plt.title("UMAP 2D Visualization")
plt.xlabel("UMAP Dimension 1")
plt.ylabel("UMAP Dimension 2")
plt.show()
elif pca_embedding.shape[1] == 3:
# 3D plot
fig = plt.figure(figsize=(14, 6))
ax = fig.add_subplot(121, projection='3d') # 3D plot
ax.scatter(pca_embedding[:, 0], pca_embedding[:, 1], pca_embedding[:, 2], alpha=0.1)
ax.set_title("UMAP 3D Visualization")
ax.set_xlabel("UMAP Dimension 1")
ax.set_ylabel("UMAP Dimension 2")
ax.set_zlabel("UMAP Dimension 3")
# 2D plot of first 2 dimensions
ax2 = fig.add_subplot(122)
ax2.scatter(pca_embedding[:, 0], pca_embedding[:, 1], alpha=0.1)
ax2.set_title("UMAP 2D Visualization (1st and 2nd dimensions)")
ax2.set_xlabel("UMAP Dimension 1")
ax2.set_ylabel("UMAP Dimension 2")
plt.show()
def fit_umap_and_hdbscan(dataset, min_clusters=10, max_clusters=40, supervised=False):
'''
Finds an optimal UMAP fit on the data whilst keeping a limit on the amount of clusters
that are obtained with HDBSCAN.
Parameters:
dataset (pd.DataFrame): The data to apply UMAP and clustering on.
min_clusters (int): Minimum amount of clusters constraint on clustering.
max_clusters (int): Maximum amount of clusters constraint on clustering.
supervised (bool): Boolean that determines whether UMAP is fitted supervised or not.
Returns:
best_umap_embedding: The UMAP result that gave the best silhouette score.
best_cluster_labels: The cluster labels that gave the ebst silhouette score.
'''
y_labels = None
if supervised:
y_labels = dataset['freezing_label']
# Define parameter grids
umap_param_grid = {'n_neighbors': [5, 10, 15],
'min_dist': [0.1, 0.5, 0.8],
'n_components': [2, 3]}
hdbscan_param_grid = {'min_cluster_size': np.arange(20, 80, 10),
'min_samples': np.arange(20, 80, 10)}
best_score = -1
# Turn off userwarnings for cleaner outputs
warnings.filterwarnings("ignore", category=UserWarning)
# Generate all possible combinations of hyperparameter values
umap_grid = ParameterGrid(umap_param_grid)
# Grid search loop
for umap_params in tqdm(umap_grid, total=len(list(umap_grid)), desc="UMAP Parameter Grid Search"):
umap = UMAP(**umap_params)
umap_embedding = umap.fit_transform(dataset.iloc[:, 1:-2], y=y_labels)
# Generate all possible combinations of hyperparameter values for HDBSCAN
hdbscan_grid = ParameterGrid(hdbscan_param_grid)
for hdbscan_params in hdbscan_grid:
# Perform HDBSCAN clustering
cluster_labels = HDBSCAN(**hdbscan_params).fit_predict(umap_embedding)
# Check the number of clusters and apply constraints
num_clusters = len(np.unique(cluster_labels))
# Update best parameters if the amount of clusters is within the set limits
if num_clusters <= max_clusters and num_clusters >= min_clusters:
# Calculate silhouette score from HDBSCAN clustering
silhouette = silhouette_score(umap_embedding, cluster_labels)
if silhouette > best_score:
# Calculate reconstruction error
reconstructed_data = umap.inverse_transform(umap_embedding)
error = mean_squared_error(dataset.iloc[:, 1:-2], reconstructed_data)
# Calculate preservation of distances: trustworthiness and continuity
# original_distances = pairwise_distances(dataset.iloc[:, 1:-2], metric='euclidean')
# trustworthiness_measure = trustworthiness(original_distances, umap_embedding, metric='euclidean')
# continuity_measure = continuity(original_distances, umap_embedding, metric='euclidean')
# Update best parameters if the current iteration improves the score
best_score = silhouette
best_UMAP_params = umap_params
best_hdbscan_params = hdbscan_params
best_umap_embedding = umap_embedding
best_cluster_labels = cluster_labels
# Reset userwarnings back to default
warnings.resetwarnings()
print("Best UMAP Parameters:", best_UMAP_params)
print("Best HDBSCAN Parameters:", best_hdbscan_params)
print(f"\nReconstruction error: {error}")
# print(f"Preservation of distances: Trustworthiness = {trustworthiness_measure}, continuity = {continuity_measure}")
print(f"Silhouette score: {best_score}")
print("\nAmount of clusters:", len(np.unique(best_cluster_labels)))
return best_umap_embedding, best_cluster_labels
def plot_umap_and_hdbscan_results(umap_embedding, cluster_labels, dataset):
'''
Plots various results for both UMAP and HDBSCAN clustering.
Parameters:
umap_embedding: Predicted UMAP values.
cluster_labels: HDBSCAN cluster labels.
dataset (pd.DataFrame): Frame dataset.
Returns:
None
'''
# Plot UMAP
print("These plots show the data spatially reduced using UMAP:")
visualize_umap(umap_embedding)
# Plot HDBSCAN
print("These plots show the same UMAP data but clustered using HDBSCAN:")
color_mapping = visualize_hdbscan(umap_embedding, cluster_labels)
# Plot barcodes
print("The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. \nThe barcodes also show the freezing frames using black markers.")
print("The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). \n- FRC is the percentage of frames within a cluster that are freezing frames. \n- CFC is the percentage of total freezing frames that belong to a specific cluster.")
data = dataset[:len(cluster_labels)].copy()
plot_combined_timeline_for_clusters(data, cluster_labels, color_mapping)
# Plot heatmap of cluster distances
print("This plot shows a heatmap of the pairwise distances between cluster means:")
plot_cluster_distances(umap_embedding, cluster_labels)
def visualize_umap(umap_embedding):
'''
Plots the given UMAP embedding in a 2D plot if the embedding has 2 components
and in both 2D and 3D plots if the embedding has 3 components.
Parameters:
umap_embedding: Predicted UMAP values.
Returns:
None
'''
if umap_embedding.shape[1] == 2:
# 2D plot
plt.figure(figsize=(8, 6))
plt.scatter(umap_embedding[:, 0], umap_embedding[:, 1], alpha=0.1)
plt.title("UMAP 2D Visualization")
plt.xlabel("UMAP Dimension 1")
plt.ylabel("UMAP Dimension 2")
plt.show()
elif umap_embedding.shape[1] == 3:
# 3D plot
fig = plt.figure(figsize=(14, 6))
ax = fig.add_subplot(121, projection='3d') # 3D plot
ax.scatter(umap_embedding[:, 0], umap_embedding[:, 1], umap_embedding[:, 2], alpha=0.1)
ax.set_title("UMAP 3D Visualization")
ax.set_xlabel("UMAP Dimension 1")
ax.set_ylabel("UMAP Dimension 2")
ax.set_zlabel("UMAP Dimension 3")
# 2D plot of first 2 dimensions
ax2 = fig.add_subplot(122)
ax2.scatter(umap_embedding[:, 0], umap_embedding[:, 1], alpha=0.1)
ax2.set_title("UMAP 2D Visualization (1st and 2nd dimensions)")
ax2.set_xlabel("UMAP Dimension 1")
ax2.set_ylabel("UMAP Dimension 2")
plt.show()
def visualize_hdbscan(umap_embedding, cluster_labels):
'''
Plots HDBSCAN cluster results.
Parameters:
umap_embedding: Predicted UMAP values.
cluster_labels: HDBSCAN cluster labels.
Returns:
None:
'''
# Number of clusters found (excluding noise points)
num_clusters = len(np.unique(cluster_labels[cluster_labels != -1]))
# Extract unique cluster labels (excluding noise points)
unique_clusters = np.unique(cluster_labels[cluster_labels != -1])
# Assign different colors to clusters
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_clusters)))
color_mapping = dict(zip(unique_clusters, colors))
# Display the number of clusters
print(f"Number of clusters (excluding noise): {num_clusters}")
if umap_embedding.shape[1] == 2:
# 2D plot
plt.figure(figsize=(8, 6))
for cluster_label, color in color_mapping.items():
cluster_indices = np.where(cluster_labels == cluster_label)
plt.scatter(
umap_embedding[cluster_indices, 0],
umap_embedding[cluster_indices, 1],
c=[color],
s=50,
alpha=0.7
)
# Add labels and title
plt.xlabel('UMAP Dimension 1')
plt.ylabel('UMAP Dimension 2')
plt.title('HDBSCAN Clustering Results in 2D')
plt.grid(True)
plt.show()
elif umap_embedding.shape[1] == 3:
# 3D plot
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(121, projection='3d')
for cluster_label, color in color_mapping.items():
cluster_indices = np.where(cluster_labels == cluster_label)
ax.scatter(
umap_embedding[cluster_indices, 0],
umap_embedding[cluster_indices, 1],
umap_embedding[cluster_indices, 2],
c=[color],
s=50,
alpha=0.7
)
# Add labels and title
ax.set_xlabel('UMAP Dimension 1')
ax.set_ylabel('UMAP Dimension 2')
ax.set_zlabel('UMAP Dimension 3')
ax.set_title('HDBSCAN Clustering Results in 3D')
ax2 = fig.add_subplot(122)
for cluster_label, color in color_mapping.items():
cluster_indices = np.where(cluster_labels == cluster_label)
ax2.scatter(umap_embedding[cluster_indices, 0], umap_embedding[cluster_indices, 1], label=f'Cluster {cluster_label}', c=[color], s=50)
ax2.set_xlabel('UMAP Dimension1')
ax2.set_ylabel('UMAP Dimension2')
ax2.set_title('HDBSCAN Clustering Results in 2D')
plt.tight_layout()
plt.show()
return color_mapping
def plot_cluster_distances(umap_embedding, cluster_labels):
'''
Plots a matrix showing the pairwise distances between the cluster means.
Parameters:
umap_embedding: Predicted UMAP values.
cluster_labels: HDBSCAN cluster labels.
Returns:
None
'''
# Calculate cluster means in the UMAP space
unique_clusters = np.unique(cluster_labels)
cluster_means_umap = np.array([np.mean(umap_embedding[cluster_labels == label], axis=0) for label in unique_clusters])
# Calculate pairwise distances between cluster means
distances = pairwise_distances(cluster_means_umap, metric='euclidean')
# Display the distance matrix as a table
# Calculate dynamic figure size based on the number of clusters
figsize_multiplier = max(1, len(unique_clusters) / 20) # You can adjust the factor as needed
fig, ax = plt.subplots(figsize=(8 * figsize_multiplier, 6 * figsize_multiplier)) # Adjust the values as needed
cax = ax.matshow(distances, cmap='viridis')
fig.colorbar(cax)
# Add labels
ax.set_xticks(np.arange(len(unique_clusters)))
ax.set_yticks(np.arange(len(unique_clusters)))
ax.set_xticklabels(unique_clusters)
ax.set_yticklabels(unique_clusters)
# Add text annotations
for i in range(len(unique_clusters)):
for j in range(len(unique_clusters)):
ax.text(j, i, f'{distances[i, j]:.1f}', ha='center', va='center', color='white' if 0.7 > distances[i, j] > 0 else 'black', fontsize=8)
plt.xlabel('Cluster')
plt.ylabel('Cluster')
plt.title('Cluster Mean Distances in UMAP Space')
plt.show()
def plot_combined_timeline_for_clusters(data, cluster_labels, color_mapping=None):
'''
Generate a combined timeline for clusters in the given data.
Parameters:
data (pd.DataFrame): DataFrame containing frame data, including 'frame_number', and 'freezing_label'.
cluster_labels: HDBSCAN cluster labels.
color_mapping (dict): Mapping of cluster labels to colors.
Returns:
None
'''
# Add cluster labels to the frame data
data['cluster_label'] = cluster_labels
# Filter data to exclude noise cluster (-1)
filtered_data = data[data['cluster_label'] != -1]
# Extract unique cluster labels
unique_clusters = np.unique(cluster_labels[cluster_labels != -1])
# Get the 10 largest clusters
largest_clusters = filtered_data['cluster_label'].value_counts().nlargest(10).index.tolist()
# Create a figure with subplots for individual clusters and a single subplot for all clusters
num_rows_individual = (len(largest_clusters) + 1) // 2
num_cols_individual = 2
num_rows_combined = 1
num_cols_combined = 2
# Set the overall figure size
# fig, axs = plt.subplots(num_rows_individual + num_rows_combined, num_cols_combined, figsize=(12, 14))
fig = plt.figure(figsize=(12, 14))
fig.suptitle(f'Timeline of Clusters (Excluding Noise)', fontsize=16)
x_min = 0
x_max = len(data)
if color_mapping is None:
# Assign different colors to clusters
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_clusters)))
color_mapping = dict(zip(unique_clusters, colors))
# Plot individual timelines for the 10 largest clusters
for idx, cluster_label in enumerate(largest_clusters):
cluster_indices = filtered_data[filtered_data['cluster_label'] == cluster_label]
color = color_mapping.get(cluster_label, 'gray')
frame_numbers = cluster_indices['frame_number'].values
freezing_frame_numbers = cluster_indices[cluster_indices['freezing_label'] == 1]['frame_number'].values
percentage_freezing = (len(freezing_frame_numbers) / len(frame_numbers)) * 100
total_freezing_frames = len(filtered_data[filtered_data['freezing_label'] == 1])
# Calculate Cluster Freezing Contribution
cluster_freezing_contribution = (len(freezing_frame_numbers) / total_freezing_frames) * 100
row = idx // num_cols_individual
col = idx % num_cols_individual
ax = plt.subplot2grid((6, 2), (row, col))
ax.set_title(f'Cluster {cluster_label}, FRC: {percentage_freezing:.2f}%, 'f'CFC: {cluster_freezing_contribution:.2f}%')
ax.set_xlabel('Frame Number')
ax.set_xlim(x_min, x_max)
ax.set_yticks([])
# Plot freezing points as markers or lines at the top
ax.scatter(freezing_frame_numbers, [1] * len(freezing_frame_numbers),
marker='v', color='black', label='Freezing Point', zorder=20)
# Plot colored bars for each frame in the cluster
for frame_number in frame_numbers:
ax.axvspan(frame_number, frame_number, alpha=0.5, color=color)
# Plot combined timeline for all clusters
ax_combined = plt.subplot2grid((6, 2), (5, 0), colspan=2)
ax_combined.set_title(f'Combined Timeline for All Clusters')
ax_combined.set_xlabel('Frame Number')
ax_combined.set_yticks([])
# Plot colored bars for each frame in all clusters
for cluster_label in unique_clusters:
cluster_indices = filtered_data[filtered_data['cluster_label'] == cluster_label]
color = color_mapping.get(cluster_label, 'gray')
frame_numbers = cluster_indices['frame_number'].values
freezing_frame_numbers = cluster_indices[cluster_indices['freezing_label'] == 1]['frame_number'].values
# Plot freezing points as markers or lines at the top
ax_combined.scatter(freezing_frame_numbers, [1] * len(freezing_frame_numbers),
marker='v', color='black', label='Freezing Point', zorder=20)
# Plot colored bars for each frame in the cluster
for frame_number in frame_numbers:
ax_combined.axvspan(frame_number, frame_number, alpha=0.5, color=color, zorder=5)
# Adjust layout and spacing
plt.tight_layout(rect=[0, 0, 1, 0.95])
# Display the timeline
plt.show()
def show_frames_for_cluster(input_video_path, cluster_label, dataset, cluster_labels):
'''
Show frames from the input video for the specified cluster in a notebook.
Parameters:
input_video_path (str): Path to the input video file.
cluster_label (int): Cluster label for which frames should be displayed.
frame_data (pd.DataFrame): DataFrame containing frame data including 'frame_number' and 'cluster_label'.
Returns:
None
'''
# Add cluster labels to the frame data
frame_data = dataset[:len(cluster_labels)].copy()
frame_data['cluster_label'] = cluster_labels
# Extract the mouse number from the data
mouse_no = round(frame_data['mouse_no'].unique().item(), 1)
# Open the input video using OpenCV
video_filename = f"/Animal_{str(mouse_no).replace('.', '_')}DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4"
video_path = input_video_path + video_filename
# Open the input video using OpenCV
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print("Error: Could not open video file.")
return
# Filter data points for the specified cluster
cluster_data = frame_data[frame_data['cluster_label'] == cluster_label]
# Create a list to store frames
frames = []
# Loop through the frame numbers in the cluster
for _, row in cluster_data.iterrows():
frame_number = int(row['frame_number'])
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number - 1)
ret, frame = cap.read()
if ret:
frames.append(frame)
# Release the video capture object
cap.release()
# Create a GIF from the frames
gif_filename = f'cluster_{cluster_label}_frames.gif'
imageio.mimsave(gif_filename, frames, duration=0.1) # Adjust duration as needed
# Display the GIF in the notebook
display(Image(filename=gif_filename))
# Remove the temporary GIF file
os.remove(gif_filename)
class SeqDataset(Dataset):
def __init__(self, device, dataframe, seq_size, pred_window, shift_size):
super(SeqDataset, self).__init__()
self.device = device
self.seq_size = seq_size # Input sequence length
self.shift_size = 25 # Shift for the start of each new sequence
self.pred_window = pred_window # Additional frames for prediction
self.dataframe = dataframe
def __len__(self):
# The total number of sequences is adjusted for shifting the sequence start by self.shift_size
return (len(self.dataframe) - self.seq_size - self.pred_window) // self.shift_size + 1
def __getitem__(self, idx):
# Calculate the actual starting index for the sequence based on the shift size
start_idx = idx * self.shift_size
end_idx = start_idx + self.seq_size
target_end_idx = end_idx + self.pred_window
# Ensure we don't exceed the bounds of the dataframe
if target_end_idx > len(self.dataframe):
target_end_idx = len(self.dataframe)
end_idx = target_end_idx - self.pred_window
in_seq = torch.tensor(self.dataframe.iloc[start_idx:end_idx].values,
dtype=torch.float, device=self.device)
target_seq = torch.tensor(self.dataframe.iloc[end_idx:target_end_idx].values,
dtype=torch.float, device=self.device)
return in_seq, target_seq
class Model(nn.Module):
def __init__(self, dropout_prob, hidden_size, layer_num, input_size, output_size):
super(Model, self).__init__()
self.gru = nn.GRU(input_size, hidden_size, layer_num, batch_first=True, dropout=dropout_prob)
self.linear = nn.Linear(hidden_size, output_size)
def forward(self, in_sequence, hidden_state=None):
hidden_seq, hidden_state = self.gru(in_sequence, hidden_state)
out_seq = self.linear(hidden_seq)
return out_seq, hidden_state
def preprocess_data(dataframe, seq_size, pred_window, shift_size, batch_size, device):
'''
Processes the data for training a neural network.
Parameters:
dataframe (pd.DataFrame): The original dataset.
seq_size (int): The amount of frames in one input sequence.
pred_window (int): How many frames into the future should be predicted.
shift_size (int): The amount of frames to shift by for the next input.
batch_size (int): Batch size for the dataloaders.
device: Which device to use (either CPU or CUDA).
Returns:
torch DataLoader: Returns dataloader for training the model.
'''
# Filter the data by only selecting columns containing 'x' or 'y' in the title
# filtered_data = dataframe.filter(regex='x|y')
# Normalise the data
scaler = StandardScaler()
scaler.fit(dataframe.iloc[:, 1:-2])
data_scaled = scaler.transform(dataframe.iloc[:, 1:-2])
data = pd.DataFrame(data_scaled.reshape(dataframe.iloc[:, 1:-2].shape), columns=dataframe.iloc[:, 1:-2].columns)
# Create SeqDataset instances for the train, validation, and test sets
data.insert(0, 'frame_number', dataframe['frame_number'])
data['mouse_no'] = dataframe['mouse_no']
data['freezing_label'] = dataframe['freezing_label']
dataset = SeqDataset(device, data, seq_size, pred_window, shift_size)
# Create DataLoader instances for batching
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=True)
return data_loader
def train_model(model, epoch_num, optimizer, criterion, data_loader, save_dir):
'''
Trains a given model using the given parameters
model (nn.Module): A torch model that will be trained.
epoch_num (int): The amount of epochs (how many times we loop through the data) to train for.
optimizer (torch.optim): The optimizer used for training the model.
criterion (nn Loss): The loss function used to calculate the loss of the predictions.
data_loader (DataLoader): The train dataloader.
Return:
None
'''
min_loss = float("inf")
for epoch in range(epoch_num):
model.train()
train_loss = 0.0
i = 0
for in_seq, target_seq in tqdm(data_loader, desc=f"Epoch {epoch}/{epoch_num}"):
out_seq, _ = model(in_seq[:, :, 1:-2])
loss = criterion(out_seq, target_seq[:, :, 1:-2])
train_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
i += 1
train_loss /= len(data_loader)
print(f"Train loss: {train_loss:.4f}")
parent_dir = os.path.dirname(save_dir)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
torch.save(model.state_dict(), save_dir)
return
def extract_latents(model, data_loader, criterion):
'''
Runs the model on the test data and obtains the intermediate layer outputs (latents) and predictions.
'''
dataframes = []
for in_seq, target_seq in tqdm(data_loader):
# Get intermediate (latent) output
lat_seq, _ = model.gru(in_seq[:, :, 1:-2])
# Readd identifying labels (frame nr and mouse nr)
first_col = in_seq[:, :, 0].unsqueeze(2)
mouse_no_col = in_seq[:, :, -2].unsqueeze(2)
freezing_label_col = in_seq[:, :, -1].unsqueeze(2)
new_seq = torch.cat([first_col, lat_seq, mouse_no_col, freezing_label_col], dim=2)
# Convert the latent and transform to pd
tensor_np = new_seq.cpu().detach().numpy()
output = tensor_np.reshape(-1, tensor_np.shape[-1])
df = pd.DataFrame(output)
dataframes.append(df)
latents_df = pd.concat(dataframes, axis=0, ignore_index=True)
num_identifying_cols = 3 # Assuming 'frame_number' and 'mouse_no' are the first and last columns
latents_df.columns = ['frame_number'] + list(range(0, len(latents_df.columns) - num_identifying_cols)) + ['mouse_no'] + ['freezing_label']
return latents_df
def fit_hdbscan(dataset, min_clusters=10, max_clusters=40):
'''
Finds an optimal HDBSCAN fit on the data whilst keeping a limit on the amount of clusters
that are obtained with HDBSCAN.
Parameters:
dataset (pd.DataFrame): The data to apply HDBSCAN clustering on.
min_clusters (int): Minimum amount of clusters constraint on clustering.
max_clusters (int): Maximum amount of clusters constraint on clustering.
Returns:
best_cluster_labels: The cluster labels that gave the ebst silhouette score.
'''
hdbscan_param_grid = {'min_cluster_size': np.arange(5, 40, 5),
'min_samples': np.arange(5, 40, 5)}
# Generate all possible combinations of hyperparameter values for HDBSCAN
hdbscan_grid = ParameterGrid(hdbscan_param_grid)
best_score = -1
best_hdbscan_params = None
# Turn off userwarnings for cleaner outputs
warnings.filterwarnings("ignore", category=UserWarning)
# Grid search loop
for hdbscan_params in tqdm(hdbscan_grid, total=len(list(hdbscan_grid)), desc="HDBSCAN Parameter Grid Search"):
# Perform HDBSCAN clustering
cluster_labels = HDBSCAN(**hdbscan_params).fit_predict(dataset.iloc[:,1:-2])
# Check the number of clusters and apply constraints
num_clusters = len(np.unique(cluster_labels))
# Update best parameters if the amount of clusters is within the set limits
if num_clusters <= max_clusters and num_clusters >= min_clusters:
# Calculate silhouette score from HDBSCAN clustering
silhouette = silhouette_score(dataset.iloc[:,1:-2], cluster_labels)
if silhouette > best_score:
# Calculate preservation of distances: trustworthiness and continuity
# original_distances = pairwise_distances(dataset.iloc[:, 1:-2], metric='euclidean')
# trustworthiness_measure = trustworthiness(original_distances, umap_embedding, metric='euclidean')
# continuity_measure = continuity(original_distances, umap_embedding, metric='euclidean')
# Update best parameters if the current iteration improves the score
best_score = silhouette
best_hdbscan_params = hdbscan_params
best_cluster_labels = cluster_labels
warnings.resetwarnings()
if best_hdbscan_params is None:
print("Sorry no clustering parameters were found for your cluster limits. You can try lowering or raising your cluster amount limits.")
else:
print("Best HDBSCAN Parameters:", best_hdbscan_params)
# print(f"Preservation of distances: Trustworthiness = {trustworthiness_measure}, continuity = {continuity_measure}")
print(f"Silhouette score: {best_score}")
print("\nAmount of clusters:", len(np.unique(best_cluster_labels)))
return best_cluster_labels
def plot_rnn_and_hdbscan_results(latents, cluster_labels, dataset):
'''
Plots various results for both RNN and HDBSCAN clustering.
Parameters:
latents: RNN latent values in 3 dimensions.
cluster_labels: HDBSCAN cluster labels.
dataset (pd.DataFrame): Frame dataset.
Returns:
None
'''
# Plot RNN
print("These plots show the data spatially reduced using RNN:")
visualise_rnn(latents, cluster_labels)
# Plot HDBSCAN
print("These plots show the same RNN data but clustered using HDBSCAN:")
latent_array = latents.iloc[:,1:-2].to_numpy()
color_mapping = visualize_hdbscan(latent_array, cluster_labels)
# Plot barcodes
print("The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. \nThe barcodes also show the freezing frames using black markers.")
print("The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). \n- FRC is the percentage of frames within a cluster that are freezing frames. \n- CFC is the percentage of total freezing frames that belong to a specific cluster.")
data = dataset[:len(cluster_labels)].copy()
plot_combined_timeline_for_clusters(data, cluster_labels, color_mapping)
def visualise_rnn(data, cluster_labels):
'''
Visualises 3-dimensional latent space gotten fromthe intermediate RNN layers.
'''
if data.iloc[:, 1:-2].shape[-1] != 3:
print("Your latent data does not have 3 dimensions! \nPlease set the hidden_size variable to 3!")
return
else:
latent_data = data.iloc[:, 1:-2].copy()
# 3D plot
fig = plt.figure(figsize=(14, 6))
ax = fig.add_subplot(121, projection='3d') # 3D plot
ax.scatter(latent_data.iloc[:, 0], latent_data.iloc[:, 1], latent_data.iloc[:, 2], alpha=0.1)
ax.set_title("RNN Latent space 3D Visualization")
ax.set_xlabel("Latent Dimension 1")
ax.set_ylabel("Latent Dimension 2")
ax.set_zlabel("Latent Dimension 3")
# 2D plot of first 2 dimensions
ax2 = fig.add_subplot(122)
ax2.scatter(latent_data.iloc[:, 0], latent_data.iloc[:, 1], alpha=0.1)
ax2.set_title("RNN Latent space 2D Visualization (1st and 2nd dimensions)")
ax2.set_xlabel("Latent Dimension 1")
ax2.set_ylabel("Latent Dimension 2")
plt.show()
return
def downsample_data(data, step_size):
"""
Downsample a time series dataset by taking every step_size'th frame.
Parameters:
- data (pd.DataFrame): The input time series dataset.
- step_size (int): The downsampling factor, indicating how many frames to skip.
Returns:
- time_series (numpy.ndarray): The downsampled time series represented as a NumPy array.
"""
# Downsample the data by only taking every step_size'th frame
downsampled = data[::step_size].copy()
# Ensure the last frame is always included
last_frame = data.iloc[[-1]].copy()
downsampled = pd.concat([downsampled, last_frame], ignore_index=True)
# Convert dataset to array
time_series = downsampled.iloc[:,1:-2].values
return downsampled, time_series
def plot_gsbs_results(gsbs_model, time_series):
'''
Plot a series of result plots for a fitted GSBS model.
It plots the tdist curve, states over time line, correlation matrix with boundaries,
and histogram of the correlation between states.
Parameters:
- gsbs_model: The GSBS (Global Signal-Based Segmentation) model.
- time_series (numpy.ndarray): the time series data
- best_fit: index of the GSBS best fitted boundaries
Returns:
None
'''
# Get the index of the best GSBS state boundaries fit
best_fit_index = np.argmax([v if i > 10 else -float('inf') for i, v in enumerate(gsbs_model.tdists)])
# Plot the correlation matrix with the states
print("This plot shows the correlation matrix of the data with the segmented states overlaid in red.")
plot_corr_states(gsbs_model, time_series, best_fit_index)
# Plot the t-dist and states over time plots
print("These plots show the measure of fit (left) and the state transitions over time (right).\nNote that we purposely use the best fit after a minimum of 10 boundaries in order to ensure we have some meaningful states.")
fig, (ax1,ax2) = plt.subplots(1,2,figsize=(14,6))
ax1.plot(gsbs_model.tdists)
ax1.set_ylabel('t-distance for different boundaries')
ax1.set_xlabel('boundaries')
ax1.set_title('t-distance')
ax2.plot(gsbs_model.get_states(best_fit_index))
ax2.set_ylabel('state')
ax2.set_xlabel('time')
ax2.set_title('States vs time for best fit')
plt.show()
# Get best fitted patterns and plot histogram of their correlation
print("This plot shows a histogram of pattern correlations:")
patterns = gsbs_model.get_state_patterns(best_fit_index)
corr = np.corrcoef(patterns)[np.where(~np.eye(patterns.shape[0],dtype=bool))]
plt.hist(corr, bins=50)
plt.show()
def plot_corr_states(gsbs_model, time_series, best_fit_index):
"""
Plot the correlation matrix of a time series and highlight state boundaries.
Parameters:
- gsbs_model: The GSBS (Global Signal-Based Segmentation) model.
- time_series (numpy.ndarray): The time series data.
- best_fit_index: index of the GSBS best fitted boundaries
Returns:
None
"""
# Calculate the correlation matrix of the time series
corr = np.corrcoef(time_series)
# Get the state boundaries from the GSBS model
bounds = np.where(gsbs_model.all_bounds[best_fit_index] > 0)[0]
# Create a figure and axis for the plot
fig, ax = plt.subplots()
# Plot the correlation matrix
ax.imshow(corr, interpolation='none', vmin=0, vmax=1)
# Set labels for the axes
ax.set_xlabel('Timepoint')
ax.set_ylabel('Timepoint')
# Highlight state boundaries with red rectangles
for i in range(len(bounds)-1):
rect = patches.Rectangle(
(bounds[i], bounds[i]),
bounds[i + 1] - bounds[i],
bounds[i + 1] - bounds[i],
linewidth=2, edgecolor='r', facecolor='none', alpha=0.5
)
ax.add_patch(rect)
# Display the plot
plt.show()
return
def merge_similar_states(gsbs_model, threshold=0.7):
"""
Merge similar states based on a correlation threshold.
Parameters:
- states (list of numpy.ndarray): List of states represented as NumPy arrays.
- threshold (float): Correlation threshold for merging states. Defaults to 0.7.
Returns:
- merged_states (list of numpy.ndarray): List of merged states.
"""
# Get the index of the best GSBS state boundaries fit
best_fit_index = np.argmax([v if i > 10 else -float('inf') for i, v in enumerate(gsbs_model.tdists)])
states = gsbs_model.get_state_patterns(best_fit_index)
num_states = len(states)
merged_states = [state.copy() for state in states]
# Step 1: Identify groups of similar states
groups = []
for i in range(num_states):
found_group = False
for group in groups:
if np.corrcoef(states[i], group[0])[0, 1] > threshold:
group.append(states[i])
found_group = True
break
if not found_group:
groups.append([states[i]])
# Step 2: Calculate the average for each group
avg_states = []
for group in groups:
avg_state = np.mean(group, axis=0)
avg_states.append(avg_state)
# Step 3: Set states to their group's average
for i in range(num_states):
for avg_state in avg_states:
if np.corrcoef(states[i], avg_state)[0, 1] > threshold:
merged_states[i] = avg_state
break
print(f"Reduced the number of unique states from {len(np.unique(states, axis=0))} to {len(np.unique(merged_states, axis=0))}")
return merged_states
def upsample_data(df_downsampled, df_original):
'''
This function upsamples the state labelled data back to the original size.
It fills in the missing state labels by using the nearest state labels known (from labelling the downsampled data).
Parameters:
df_downsampled (pd.DataFrame): Downsampled data that at least contains columns 'frame_number' and 'state_label'.
df_original (pd.DataFrame): Original full sized dataset that contains the column 'frame_number'.
Returns:
df_merged (pd.DataFrame): Merged dataset that is the same as the original but with 'state_label' filled in based on the downsampled data.
'''
# Merge the downsampled dataset with the original dataset based on 'frame_number'
df_merged = pd.merge(df_original, df_downsampled[['frame_number', 'state_label']], on='frame_number', how='left')
labels = df_merged['state_label'].notna()
sum_labels = labels.cumsum()
def find_middle(x):
lens = len(x.index)
a = np.arange(lens)
return a // (lens/2) == 0
mask = sum_labels[~labels].groupby(sum_labels).transform(find_middle)
df_merged['state_label'] = df_merged['state_label'].where(mask, df_merged['state_label'].bfill()).ffill().bfill()
return df_merged
def print_frames_per_state(downsampled_data, dataset, gsbs_model, merged_states):
"""
Prints the start and end frames for consecutive sequences of states in a DataFrame.
Parameters:
- df (pd.DataFrame): The DataFrame containing the data.
Returns:
None
"""
# Get the index of the best GSBS state boundaries fit
best_fit_index = np.argmax([v if i > 10 else -float('inf') for i, v in enumerate(gsbs_model.tdists)])
# Extract state labels and add to dataset
states = gsbs_model.get_states(best_fit_index)
states = states - 1
unique_arrays, indices = np.unique(merged_states, axis=0, return_inverse=True)
state_labels = [indices[i] for i in states]
downsampled_data['state_label'] = state_labels
# Upsample the data
dataset = upsample_data(downsampled_data, dataset)
# Identify consecutive sequences of states
state_sequences = {}
current_state = None
current_sequence = []
for index, state in enumerate(dataset['state_label']):
if current_state is None or state == current_state:
current_sequence.append(index)
else:
state_sequences.setdefault(current_state, []).append(current_sequence)
current_sequence = [index]
current_state = state
if current_state is not None:
state_sequences.setdefault(current_state, []).append(current_sequence)
# Extract and print start and end frames for each state's sequences
state_data = {}
for state, sequences in state_sequences.items():
state_data[state] = []
for sequence in sequences:
data_subset = dataset.loc[sequence, 'frame_number'].tolist()
state_data[state].append(data_subset)
for state, data_sequences in state_data.items():
print(f"Sequences for state '{state}': ")
for sequence in data_sequences:
print(f"Start frame: {sequence[0]}, end frame: {sequence[-1]}")
print("")
return state_data, state_labels, dataset
def plot_barcodes(merged_states, downsampled):
'''
Plots two barcode representations of the segmented states:
Parameters:
merged_states (np.array): List of merged states.
downsampled (pd.DataFrame): Downsampled frame data containing columns 'state_label' and 'freezing_label'.
Returns:
None
'''
# Plot barcode that shows just the state order and occurences
print("This plot shows states by colour, showing their order and recurrence throughout the data:")
color_mapping = plot_barcode(merged_states)
# Plot barcode that shows states over time and freezing frames
print("This plot shows the states over the frames and includes markers to show the freezing frames:")
plot_barcode_with_frames_and_freezing(merged_states, downsampled, color_mapping)
def plot_barcode(merged_states):
"""
Plot a barcode representation of indices based on unique arrays.
Parameters:
- merged_states (list of numpy.ndarray): List of merged states.
Returns:
None
"""
# Identify unique arrays and their corresponding indices
unique_arrays, indices = np.unique(merged_states, axis=0, return_inverse=True)
# Count occurrences of each unique index
unique_indices, counts = np.unique(indices, return_counts=True)
# Generate colors for each unique index
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_indices)))
# Create a colormap mapping indices to colors
colormap = dict(zip(unique_indices, colors))
# Map indices to colors in an array
colors_array = np.array([colormap[index] for index in indices])
# Create a barcode plot
fig, ax = plt.subplots(figsize=(10, 1))
ax.imshow([colors_array], aspect='auto', extent=[0, len(indices), 0, 1])
# Set plot properties
ax.set_yticks([])
ax.set_xlabel('Index')
ax.set_title('Barcode Plot of state patterns')
# Display the plot
plt.show()
return colormap
def plot_barcode_with_frames_and_freezing(merged_states, state_labels_df, color_mapping=None):
"""
Plot a barcode representation of indices based on unique arrays with the full length of patterns.
Includes black markers for freezing frames.
Parameters:
- merged_states (list of numpy.ndarray): List of merged states.
- state_labels_df (pd.DataFrame): DataFrame containing 'state_label', 'freezing_label', and 'frame_number' with index as the frame numbers.
Returns:
None
"""
# Identify unique arrays and their corresponding indices
unique_arrays, indices = np.unique(merged_states, axis=0, return_inverse=True)
# Create a colormap mapping state labels to colors
if color_mapping is None:
unique_states = np.unique(state_labels_df['state_label'])
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_states)))
color_mapping = dict(zip(unique_states, colors))
# Map state labels to colors in an array
colors_array = np.array([color_mapping[label] for label in state_labels_df['state_label']])
# Use 'frame_number' column as x-axis values
frame_numbers = state_labels_df['frame_number'].values
# Create a barcode plot
fig, ax = plt.subplots(figsize=(10, 1)) # Adjust the height to accommodate both indices and frames
ax.imshow([colors_array], aspect='auto', extent=[frame_numbers.min(), frame_numbers.max(), 0, 2])
# Plot black markers for freezing frames
freezing_frame_numbers = state_labels_df[state_labels_df['freezing_label'] == 1]['frame_number'].values
ax.scatter(freezing_frame_numbers, [1] * len(freezing_frame_numbers),
marker='v', color='black', label='Freezing Point')
# Set plot properties
ax.set_xlabel('Frame Number')
ax.set_yticks([])
ax.set_title('Barcode Plot of States over Frames with Freezing')
# Display the plot
plt.show()
def plot_hdbscan_bp(dataset, embedding_result, cluster_labels):
'''
'''
# Create uniform color mapping for all plots
unique_clusters = np.unique(cluster_labels[cluster_labels != -1])
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_clusters)))
color_mapping = dict(zip(unique_clusters, colors))
# Plot barcode plot
print("The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. \nThe barcodes also show the freezing frames using black markers.")
print("The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). \n- FRC is the percentage of frames within a cluster that are freezing frames. \n- CFC is the percentage of total freezing frames that belong to a specific cluster.")
data = dataset[:len(cluster_labels)].copy()
plot_combined_timeline_for_clusters(data, cluster_labels, color_mapping)
# Transform color mapping to hex format
hex_colors = [to_hex(color) for color in color_mapping.values()]
# Plot chord diagram
print("This chord diagram shows all the connections between the different clusters.")
create_chord_diagram(data, hex_colors)
# Plot trajectories
print("These plot shows all transitions between frames (in 3D and 2D).")
plot_trajectories(cluster_labels, embedding_result, color_mapping=color_mapping)
print("This transition matrix is another way of viewing the connections between the clusters.\nThe values in the squares show the amount of transitions between the current and target cluster and the total amount of transitions from this current cluster to all clusters (within brackets).")
plot_transition_matrix(cluster_labels)
def plot_transition_matrix(cluster_labels):
labels_list = []
for label in cluster_labels:
if label != -1 and (not labels_list or label != labels_list[-1]):
labels_list.append(label)
clusters = np.unique(labels_list)
num_clusters = len(clusters)
transition_matrix = np.zeros((num_clusters, num_clusters))
cluster_index = {cluster: i for i, cluster in enumerate(clusters)}
for (current_cluster, next_cluster) in zip(labels_list, labels_list[1:]):
i = cluster_index[current_cluster]
j = cluster_index[next_cluster]
transition_matrix[i, j] += 1
# Normalize to get probabilities
transition_matrix_prob = transition_matrix / np.sum(transition_matrix, axis=1, keepdims=True)
# Calculate dynamic figure size based on the number of clusters
figsize_multiplier = max(1, num_clusters / 15) # You can adjust the factor as needed
fig, ax = plt.subplots(figsize=(8 * figsize_multiplier, 6 * figsize_multiplier)) # Adjust the values as needed
im = ax.imshow(transition_matrix_prob, cmap='hot', interpolation='nearest')
# Add text annotations for each square
for i in range(len(clusters)):
for j in range(len(clusters)):
count = int(transition_matrix[i, j])
text = f'{count}\n({int(np.sum(transition_matrix[i, :]))})'
ax.text(j, i, text, ha='center', va='center', color='white' if 0.6 > transition_matrix_prob[i, j] > 0 else 'black', fontsize=10)
plt.colorbar(im)
plt.xticks(np.arange(len(clusters)), clusters)
plt.yticks(np.arange(len(clusters)), clusters)
plt.xlabel('Next Cluster')
plt.ylabel('Current Cluster')
plt.title('Cluster Transition Matrix')
plt.show()
def plot_trajectories(cluster_labels, data, limit=None, show_order=False, color_mapping=None):
'''
Plots the HDBSCAN clusters but with the frame transition trajectories between the points.
'''
# Get cluster labels from clusterer
# cluster_labels = clusterer.labels_
# Limit data based on given amount of seconds
if limit is not None:
frames = limit * 25
data = data[:frames]
cluster_labels = cluster_labels[:frames]
# Determine the number of dimensions in the data
num_dimensions = data.shape[1]
# Plotting
if num_dimensions == 2:
# 2D plot
fig, ax = plt.subplots(figsize=(8, 8)) # Square plot for 2D
for cluster_label, color in color_mapping.items():
cluster_indices = np.where(cluster_labels == cluster_label)
ax.scatter(data[cluster_indices, 0], data[cluster_indices, 1], label=f'Cluster {cluster_label}', c=[color], s=50)
for i in range(len(data) - 1):
ax.plot([data[i, 0], data[i+1, 0]],
[data[i, 1], data[i+1, 1]],
c='black', linestyle='dashed', linewidth=0.5, alpha=0.5)
if show_order:
cmap = cm.get_cmap('viridis', len(data))
for i in range(0, len(data), 10):
color = cmap(i / len(data))
ax.text(data[i, 0], data[i, 1], f'{i}', color=color)
ax.set_xlabel('UMAP Dimension 1')
ax.set_ylabel('UMAP Dimension 2')
ax.set_title('HDBSCAN Clustering Results in 2D with transition trajectories')
elif num_dimensions == 3:
# 3D plot
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(121, projection='3d')
for cluster_label, color in color_mapping.items():
cluster_indices = np.where(cluster_labels == cluster_label)
ax.scatter(data[cluster_indices, 0], data[cluster_indices, 1], data[cluster_indices, 2], label=f'Cluster {cluster_label}', c=[color], s=50, alpha=0.7)
for i in range(len(data) - 1):
ax.plot([data[i, 0], data[i+1, 0]],
[data[i, 1], data[i+1, 1]],
[data[i, 2], data[i+1, 2]],
c='black', linestyle='dashed', linewidth=0.5, alpha=0.5)
if show_order:
cmap = cm.get_cmap('viridis', len(data))
for i in range(0, len(data), 10):
color = cmap(i / len(data))
ax.text(data[i, 0], data[i, 1], data[i, 2], f'{i}', color=color)
ax.set_xlabel('UMAP Dimension 1')
ax.set_ylabel('UMAP Dimension 2')
ax.set_zlabel('UMAP Dimension 3')
ax.set_title('HDBSCAN Clustering Results in 3D with transition trajectories')
# 2D plot
ax2 = fig.add_subplot(122)
for cluster_label, color in color_mapping.items():
cluster_indices = np.where(cluster_labels == cluster_label)
ax2.scatter(data[cluster_indices, 0], data[cluster_indices, 1], label=f'Cluster {cluster_label}', c=[color], s=50)
for i in range(len(data) - 1):
ax2.plot([data[i, 0], data[i+1, 0]],
[data[i, 1], data[i+1, 1]],
c='black', linestyle='dashed', linewidth=0.5, alpha=0.5)
if show_order:
for i in range(0, len(data), 10):
color = cmap(i / len(data))
ax2.text(data[i, 0], data[i, 1], f'{i}', color=color)
ax2.set_xlabel('UMAP Dimension 1')
ax2.set_ylabel('UMAP Dimension 2')
ax2.set_title('HDBSCAN Clustering Results in 2D with transition trajectories')
else:
raise ValueError("Unsupported number of dimensions. The function supports 2D and 3D data.")
plt.tight_layout()
plt.show()
def create_chord_diagram(latent_frames, color_map):
"""
Create a Chord diagram to visualize connections between successive frames and cluster labels.
Parameters:
- latent_frames (pd.DataFrame): DataFrame containing 'frame_number' and 'cluster_label' columns.
Returns:
- hv.Chord: Chord diagram representing connections between clusters.
"""
# Enable the Bokeh notebook extension
hv.extension('bokeh')
hv.output(size=200)
# Exclude the noise cluster (-1)
filtered_frames = latent_frames[latent_frames['cluster_label'] != -1]
# Create a DataFrame to represent the connections between successive frames
connections = pd.DataFrame({
'source': filtered_frames['cluster_label'].shift(1),
'target': filtered_frames['cluster_label']
}).dropna()
# Count the number of connections between each pair of clusters
connection_counts = connections.groupby(['source', 'target']).size().reset_index(name='value')
# Create a Chord diagram using Holoviews
chord = hv.Chord(connection_counts)
chord.opts(
opts.Chord(cmap=color_map, edge_cmap=color_map, edge_color=dim('source').str(),
labels='source', node_color=dim('index').str()))
show(hv.render(chord))
def plot_gsbs_bp(merged_states, downsampled):
'''
Plots two barcode representations of the segmented states:
Parameters:
merged_states (np.array): List of merged states.
downsampled (pd.DataFrame): Downsampled frame data containing columns 'state_label' and 'freezing_label'.
Returns:
None
'''
# Plot barcode that shows just the state order and occurences
print("This plot shows states by colour, showing their order and recurrence throughout the data:")
color_mapping = plot_barcode(merged_states)
# Plot barcode that shows states over time and freezing frames
print("This plot shows the states over the frames and includes markers to show the freezing frames:")
plot_barcode_with_frames_and_freezing(merged_states, downsampled, color_mapping)
# Transform color mapping to hex format
hex_colors = [to_hex(color) for color in color_mapping.values()]
# Plot chord diagram showing transitions between states
print("This chord diagram shows all the connections between the different states")
plot_gsbs_chord(merged_states, hex_colors)
# Plot transition matrix
print("This transition matrix is another way of viewing the connections between the states.\nThe values in the squares show the amount of transitions between the current and target state and the total amount of transitions from this current state to all states (within brackets).")
plot_transition_matrix_and_graph(merged_states)
# Find recurring patterns of states in the data
unique_arrays, indices = np.unique(merged_states, axis=0, return_inverse=True)
recurring_patterns = find_recurring_patterns(indices, pattern_length=10)
# Print the recurring patterns in a nice way
print("Here we'll print all recurring patterns and where they occur in the order of states:\n")
for pattern, index in recurring_patterns.items():
print(f"Pattern: {pattern}, Occurrences at indices: {index}")
return recurring_patterns
def plot_gsbs_chord(merged_states, hex_colors):
unique_arrays, indices = np.unique(merged_states, axis=0, return_inverse=True)
# Enable the Bokeh notebook extension
hv.extension('bokeh')
hv.output(size=200)
data = {'source': indices, 'target': np.roll(indices, -1)}
df = pd.DataFrame(data)
chord = hv.Chord(df)
chord.opts(
opts.Chord(cmap=hex_colors, edge_cmap=hex_colors, edge_color=dim('source').str(), labels='source', node_color=dim('index').str()))
show(hv.render(chord))
def find_recurring_patterns(indices, pattern_length=4):
patterns_dict = {}
for j in range(2, pattern_length + 1):
for i in range(len(indices) - j + 1):
pattern = tuple(indices[i:i + j])
if pattern in patterns_dict:
if i not in patterns_dict[pattern]:
patterns_dict[pattern].append(i)
else:
pattern_indices = [p for p, x in enumerate(indices) if np.array_equal(np.array(indices[p:p + j]), np.array(pattern))]
if len(pattern_indices) > 1:
patterns_dict[pattern] = pattern_indices
return patterns_dict
def show_frames_for_sequence(input_video_path, sequence, dataset, state_data):
'''
Show frames from the input video for each occurrence of the specified sequence in a notebook.
Parameters:
input_video_path (str): Path to the input video file.
sequence (list): List of state labels representing the sequence to visualize.
dataset (pd.DataFrame): DataFrame containing frame data including 'frame_number' and 'state_label'.
Returns:
None
'''
# print(input_video_path)
mouse_no = round(dataset['mouse_no'].unique().item(), 1)
video_filename = f"/Animal_{str(mouse_no).replace('.', '_')}DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4"
video_path = input_video_path + video_filename
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print("Error: Could not open video file.")
return
all_occurrences_frames = []
# Go through the dictionary of states to find the given sequence
for frame_sequence in state_data.get(sequence[0], []):
found_occurence = True
for i in range(1, len(sequence)):
next_frame_sequence = state_data[sequence[i]]
if any(frame_sequence[-1]+1 == frame[0] for frame in next_frame_sequence):
for frame in next_frame_sequence:
if frame_sequence[-1]+1 == frame[0]:
frame_sequence = frame_sequence+frame
else:
found_occurence = False
break
if found_occurence:
frames = []
for frame_number in frame_sequence:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number - 1)
ret, frame = cap.read()
if ret:
frames.append(frame)
all_occurrences_frames.append(frames)
# Create and display GIFs for each occurrence
for idx, frames in enumerate(all_occurrences_frames):
gif_filename = f'sequence_{idx + 1}_frames.gif'
imageio.mimsave(gif_filename, frames, duration=0.04)
display(Image(filename=gif_filename))
os.remove(gif_filename)
def plot_transition_matrix_and_graph(merged_states):
unique_arrays, indices = np.unique(merged_states, axis=0, return_inverse=True)
states = np.unique(indices)
num_states = len(states)
transition_matrix = np.zeros((num_states, num_states))
state_index = {state: i for i, state in enumerate(states)}
for (current_state, next_state) in zip(indices, indices[1:]):
i = state_index[current_state]
j = state_index[next_state]
transition_matrix[i, j] += 1
# Normalize to get probabilities
transition_matrix_prob = transition_matrix / np.sum(transition_matrix, axis=1, keepdims=True)
fig, ax = plt.subplots(figsize=(8, 8))
im = ax.imshow(transition_matrix_prob, cmap='hot', interpolation='nearest')
# Add text annotations for each square
for i in range(len(states)):
for j in range(len(states)):
count = int(transition_matrix[i, j])
text = f'{count}\n({int(np.sum(transition_matrix[i, :]))})'
ax.text(j, i, text, ha='center', va='center', color='white' if 0.6 > transition_matrix_prob[i, j] > 0 else 'black', fontsize=8)
plt.colorbar(im)
plt.xticks(np.arange(len(states)), states)
plt.yticks(np.arange(len(states)), states)
plt.xlabel('Next State')
plt.ylabel('Current State')
plt.title('State Transition Matrix')
plt.show()
print("This plot shows a state transition graph where the states are the nodes and the transitions are the lines.\nThe transition weight is shown in the thickness of the lines (and the printed values).")
G = nx.DiGraph()
for i, state1 in enumerate(states):
for j, state2 in enumerate(states):
if transition_matrix[i, j] > 0:
G.add_edge(state1, state2, weight=round(transition_matrix[i, j], 2))
pos = nx.spring_layout(G)
weights = nx.get_edge_attributes(G, 'weight')
# Determine line thickness based on connection strength
edge_weights = [G[u][v]['weight'] * 2 for u, v in G.edges()] # Scale factor for visibility
plt.figure(figsize=(10, 10))
nx.draw(G, pos, with_labels=True, node_color='skyblue', node_size=2000, edge_color='black',
width=edge_weights, linewidths=1, font_size=15)
nx.draw_networkx_edge_labels(G, pos, edge_labels=weights)
plt.title('State Transition Graph with Line Thickness')
plt.show()
This notebook already inlcudes results results of a random mouse. \ If you want to run it for another mouse, ensure to have all necessary data in the correct places and simply follow along the notebook.
The dropdown box below allows you to select what type of data you would like to work with. \ The choices are as follows: \ Original: The original data containing solely the X and Y coordinates of the mouse's limb points.\ MouseCentric: Transformed dataset where all limb point coordinates are centered around the mouse's center. The main purpose is to disregard the mouse's spatial position. \ MouseCentricPolar: MouseCentric dataset but with Polar coordinates rather than cartesian coordinates. \ ChangeCoord: Transformed dataset that only contains the change in coordinates with respect to the previous frame. \ MouseDirectional: Transformed dataset that combines the MouseCentric and Polar transformations. The MouseCentric data is rotated using angles from the Polar dataset. The main purpose is to disregard both the mouse's spatial position and it's direction (or angle). \ MouseDirectionalPolar: MouseDirectional dataset but with Polar coordinates rather than cartesian coordinates. \ Features: Features that were engineered from the original dataset. Includes features such as mouse area change, movements, and euclidean distances.
data_options = ["Original", "MouseCentric", "MouseCentricPolar", "ChangeCoord", "MouseDirectional", "MouseDirectionalPolar", "Features"]
dropdown = widgets.Dropdown(options=data_options, description='Datatype:')
print("What type of data would you like to work with?")
display(dropdown)
What type of data would you like to work with?
Dropdown(description='Datatype:', options=('Original', 'MouseCentric', 'MouseCentricPolar', 'ChangeCoord', 'Mo…
Make sure to run all cells above this \
Set a source directory below as main_folder.
Based on your selection, running the cell below will transform your data and save it. \ Per type of data, all data is saved in folders separately per mouse, running the below cell will concatenate all mice data into one large dataset in the current working environment. \ Example of how a file is saved: "main_folder/Datasets/Original/2.4_Original_data.csv"
By changing the below variable mouse_number you can choose to work with a specific mouse. If left at None, a mouse is chosen at random.
# Set the main folder to work from. This folder should have a subfolder called 'filtered' with all the unedited mouse csv files.
# The newly transformed data will then be put in corresponding subfolders in this main folder.
main_folder = "C:/Users/Kieran/Documents/Master Thesis Data"
mouse_number = 2.4 # Change this to choose a specific mouse to work with. Leaving at None will choose a random mouse.
datatype = dropdown.value
dataset = get_dataset(main_folder, datatype, mouse_number)
Processing Files: 100%|████████████████████████████████████████████████████████████████| 98/98 [00:20<00:00, 4.76it/s]
The data for mouse 2.4 was extracted. The shape of the data: 11250 frames by 31 columns of which three are identifying columns: 'frame_number', 'mouse_no', and 'freezing_label'. The rest are features. The mouse data has a video rate of 25 frames per second.
The cell below will show a heatmap of all features over time from the currently selected mouse data.
plot_dataframe_heatmap(dataset, datatype)
For analysis of the selected mouse data we have assembled a collection of methods that can be divided into being either spatial dimensionality reduction, temporal dimensionality reduction, or a combination of both. \ The methods can be divided into the mentioned categories as follows: \ Spatial dimensionality reduction:
Temporal dimensionality reduction:
This notebook explores 4 different (combinations of) methods on the mouse data to cluster behaviour. The following index shows the different methods and allows you to choose which to explore. \ Simply click on one of the method links to jump to that section of the notebook:
The following function will fit a UMAP dimensionality reduction model on the data. It will run an optimzation loop to try different parameters and find the best fit with them based on the silhouette score. \
It will also cluster the UMAP result using HDBSCAN, which can be limited by setting the min_clusters and max_clusters parameters as desired.
embedding_result, cluster_labels = fit_umap_and_hdbscan(dataset, min_clusters=10, max_clusters=40)
UMAP Parameter Grid Search: 100%|██████████████████████████████████████████████████████| 18/18 [11:12<00:00, 37.35s/it]
Best UMAP Parameters: {'min_dist': 0.1, 'n_components': 2, 'n_neighbors': 15}
Best HDBSCAN Parameters: {'min_cluster_size': 70, 'min_samples': 40}
Reconstruction error: 347.85994726472524
Silhouette score: 0.15076306462287903
Amount of clusters: 39
The reconstruction error is calculated by attempting to reconstruct the original data from the embedded UMAP result and measuring the Mean Squared Error between this reconstruction and the original data. \ The silhouette score is a measure of how well the individual data points fit in the cluster they were put in compared to the other clusters. The score ranges from -1 to 1, where a higher value means the data point is well matched in its cluster and poorly matched to other clusters. The mean of all data points' silhouette score is taken to calculate an overall silhouette coefficient.
The function below will plot the UMAP and HDBSCAN cluster results. \ When using a 3-dimensional UMAP space, the function below will plot both a 3D plot of the results and a 2D plot of these results. Using a 2-dimensional UMAP will only plot a 2D result. \ The points in the UMAP scatter plots represent the original frames in the data but dimensionally reduced using UMAP. The coloured scatter plots show the same reduced data but with colours showing which cluster they were put in. \ The 'barcode' plots show a timeline of the video frames, using colours to show where each cluster's frames are located on the video timeline. \ Note that for the plots where we visualise clusters, we purposely leave out the noise cluster (denoted with a label of -1). This contains all data that HDBSCAN denotes as noise and does not deem fit in any cluster. \ Finally, we plot a heatmap showing the pairwise distances between the means of the clusters. This tells us which clusters are closer together than others and is useful for further analysis.
plot_umap_and_hdbscan_results(embedding_result, cluster_labels, dataset)
These plots show the data spatially reduced using UMAP:
These plots show the same UMAP data but clustered using HDBSCAN: Number of clusters (excluding noise): 38
The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. The barcodes also show the freezing frames using black markers. The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). - FRC is the percentage of frames within a cluster that are freezing frames. - CFC is the percentage of total freezing frames that belong to a specific cluster.
This plot shows a heatmap of the pairwise distances between cluster means:
The two cells below will allow you to choose a specific cluster and then visualise the video frames belonging to that cluster for you.
#### Run this cell! ####
cluster_options = np.unique(cluster_labels[cluster_labels != -1])
dropdown_cluster = widgets.Dropdown(options=cluster_options, description='Cluster:')
print("Which cluster would you like to see the video frames of?")
display(dropdown_cluster)
Which cluster would you like to see the video frames of?
Dropdown(description='Cluster:', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19…
Make sure to run the cell above to choose which cluster to view!
# The input video path should point to the directory containing the mouse mp4 videos titled as: Animal_11_4DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4
input_video_path = "../labelled_videos" # Change this if your directory is somewhere else
cluster = dropdown_cluster.value
show_frames_for_cluster(input_video_path, cluster, dataset, cluster_labels)
The cell belows shows supervised UMAP results where the freezing frames are used as labels.
supervised_embedding_result, supervised_cluster_labels = fit_umap_and_hdbscan(dataset, min_clusters=10, max_clusters=40, supervised=True)
plot_umap_and_hdbscan_results(supervised_embedding_result, supervised_cluster_labels, dataset)
UMAP Parameter Grid Search: 100%|██████████████████████████████████████████████████████| 18/18 [15:23<00:00, 51.33s/it]
Best UMAP Parameters: {'min_dist': 0.1, 'n_components': 3, 'n_neighbors': 15}
Best HDBSCAN Parameters: {'min_cluster_size': 70, 'min_samples': 30}
Reconstruction error: 330.9679692730247
Silhouette score: 0.156976580619812
Amount of clusters: 37
These plots show the data spatially reduced using UMAP:
These plots show the same UMAP data but clustered using HDBSCAN: Number of clusters (excluding noise): 36
The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. The barcodes also show the freezing frames using black markers. The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). - FRC is the percentage of frames within a cluster that are freezing frames. - CFC is the percentage of total freezing frames that belong to a specific cluster.
This plot shows a heatmap of the pairwise distances between cluster means:
The two cells below will allow you to choose a specific cluster and then visualise the video frames belonging to that cluster for you.
#### Run this cell! ####
cluster_options = np.unique(supervised_cluster_labels[supervised_cluster_labels != -1])
dropdown_cluster = widgets.Dropdown(options=cluster_options, description='Cluster:')
print("Which cluster would you like to see the video frames of?")
display(dropdown_cluster)
Which cluster would you like to see the video frames of?
Dropdown(description='Cluster:', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19…
Make sure to run the cell above to choose which cluster to view!
# The input video path should point to the directory containing the mouse mp4 videos titled as: Animal_11_4DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4
input_video_path = "../labelled_videos" # Change this if your directory is somewhere else
cluster = dropdown_cluster.value
show_frames_for_cluster(input_video_path, cluster, dataset, supervised_cluster_labels)
The below functions will train an RNN model on the chosen dataset. \ The model will take 25 frames as input sequence, and attempt to predict the next 25 frames. \ The next input sequence is shifted by 25 frames from the last. \ You can view and/or change the model architecture in the code blocks below.
# This cell sets which device to use. If a GPU is available it will choose that, otherwise it will select CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using", device)
Using cuda
# Parameters for training the RNN
dropout_prob = 0.5
epoch_num = 10
hidden_size = 16 # This defines the size of the latent values
layer_num = 2
learning_rate = 1e-3
seq_size = 25 # The amount of frames inputted as one sequence, 25 frames is 1 second of video
pred_window = 25 # The amount of frames (after the input) the model predicts, 25 frames is 1 second of video
shift_size = 25 # By how many frames each new input for the model is shifted
batch_size = 64 # The batch size for the dataloaders
# This function processes the data, splits it into train, test, and validation, normalizes it, and creates DataLoaders
data_loader = preprocess_data(dataset, seq_size, pred_window, shift_size, batch_size, device)
# Get the input and output size of our data
input_size = next(iter(data_loader))[0][:, :, 1:-2].size(-1)
output_size = next(iter(data_loader))[1][:, :, 1:-2].size(-1)
# Create our model with our parameters and data sizes
model = Model(dropout_prob, hidden_size, layer_num, input_size, output_size).to(device)
# Create a loss function and optimizer for the model
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), learning_rate)
print(model)
Model( (gru): GRU(28, 16, num_layers=2, batch_first=True, dropout=0.5) (linear): Linear(in_features=16, out_features=28, bias=True) )
# Set a save location for the model (including filename for the model)
mouse_no = dataset['mouse_no'].unique().item()
save_dir = f"../Models/rnn_{datatype}_data_1l_mouse_nr_{str(mouse_no)}_hidden_size_{str(hidden_size)}.pt"
train_model(model, epoch_num, optimizer, criterion, data_loader, save_dir)
Epoch 0/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 40.23it/s]
Train loss: 1.0296
Epoch 1/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 53.85it/s]
Train loss: 1.0115
Epoch 2/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 50.00it/s]
Train loss: 1.0002
Epoch 3/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 37.84it/s]
Train loss: 0.9912
Epoch 4/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 49.85it/s]
Train loss: 0.9838
Epoch 5/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 35.90it/s]
Train loss: 0.9762
Epoch 6/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 46.36it/s]
Train loss: 0.9703
Epoch 7/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 42.94it/s]
Train loss: 0.9647
Epoch 8/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 47.94it/s]
Train loss: 0.9600
Epoch 9/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 50.36it/s]
Train loss: 0.9555
The below functions will load in the saved model and run the full dataset through it. \ Instead of the output (predictions for future sequences) it will collect the intermediate layer outputs. \ This is what we call the latent space and where we will hopefully see (after dimensionality reduction and clustering) learned behaviours (or sequences).
# Load the saved model
model.load_state_dict(torch.load(save_dir))
model.eval()
Model( (gru): GRU(28, 16, num_layers=2, batch_first=True, dropout=0.5) (linear): Linear(in_features=16, out_features=28, bias=True) )
# Analyses the model using the test data, and obtains the performance (loss) and latent values for all sequences
latents_df = extract_latents(model, data_loader, criterion)
100%|████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 36.13it/s]
In order to visualise and cluster the latent space properly, we have to reduce the amount of dimensions. We can do this using PCA, UMAP, or by setting the hidden_size variable of the RNN to 3. This last one ensures that the latent dimensions of the RNN are already small enough to cluster directly.
#### Run this cell! ####
dim_red_options = ['PCA', 'UMAP', 'RNN']
dropdown2 = widgets.Dropdown(options=dim_red_options, description='Dimensionality reduction:')
print("What type of dimensionality reduction would you like to work with?")
display(dropdown2)
What type of dimensionality reduction would you like to work with?
Dropdown(description='Dimensionality reduction:', options=('PCA', 'UMAP', 'RNN'), value='PCA')
Make sure to run the cell above to be able to choose the dimensionality reduction method! \
To use the RNN model by itself as dimensionality reduction method, ensure that the hidden_size variable is set to 3.
dim_red_type = dropdown2.value
min_clusters = 10
max_clusters = 40
if dim_red_type == 'UMAP':
embedding_result, cluster_labels = fit_umap_and_hdbscan(latents_df, min_clusters, max_clusters)
elif dim_red_type == 'PCA':
embedding_result, cluster_labels = fit_pca_and_hdbscan(latents_df, min_clusters, max_clusters)
elif dim_red_type == 'RNN':
cluster_labels = fit_hdbscan(latents_df, min_clusters, max_clusters)
UMAP Parameter Grid Search: 100%|██████████████████████████████████████████████████████| 18/18 [07:59<00:00, 26.64s/it]
Best UMAP Parameters: {'min_dist': 0.1, 'n_components': 3, 'n_neighbors': 5}
Best HDBSCAN Parameters: {'min_cluster_size': 70, 'min_samples': 20}
Reconstruction error: 0.008973576128482819
Silhouette score: -0.061985310167074203
Amount of clusters: 33
The reconstruction error is calculated by attempting to reconstruct the original data from the embedded result and measuring the Mean Squared Error between this reconstruction and the original data. \ The silhouette score is a measure of how well the individual data points fit in the cluster they were put in compared to the other clusters. The score ranges from -1 to 1, where a higher value means the data point is well matched in its cluster and poorly matched to other clusters. The mean of all data points' silhouette score is taken to calculate an overall silhouette coefficient.
The function below will plot the UMAP and HDBSCAN cluster results. \ When using a 3-dimensional UMAP space, the function below will plot both a 3D plot of the results and a 2D plot of these results. Using a 2-dimensional UMAP will only plot a 2D result. \ The points in the UMAP scatter plots represent the original frames in the data but dimensionally reduced using UMAP. The coloured scatter plots show the same reduced data but with colours showing which cluster they were put in. \ The 'barcode' plots show a timeline of the video frames, using colours to show where each cluster's frames are located on the video timeline. \ Note that for the plots where we visualise clusters, we purposely leave out the noise cluster (denoted with a label of -1). This contains all data that HDBSCAN denotes as noise and does not deem fit in any cluster. \ Finally, we plot a heatmap showing the pairwise distances between the means of the clusters. This tells us which clusters are closer together than others and is useful for further analysis.
if dim_red_type == 'UMAP':
plot_umap_and_hdbscan_results(embedding_result, cluster_labels, dataset)
elif dim_red_type == 'PCA':
plot_pca_and_hdbscan_results(embedding_result, cluster_labels, dataset)
elif dim_red_type == 'RNN':
plot_rnn_and_hdbscan_results(latents_df, cluster_labels, dataset)
These plots show the data spatially reduced using UMAP:
These plots show the same UMAP data but clustered using HDBSCAN: Number of clusters (excluding noise): 32
The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. The barcodes also show the freezing frames using black markers. The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). - FRC is the percentage of frames within a cluster that are freezing frames. - CFC is the percentage of total freezing frames that belong to a specific cluster.
This plot shows a heatmap of the pairwise distances between cluster means:
The two cells below will allow you to choose a specific cluster and then visualise the video frames belonging to that cluster for you.
#### Run this cell! ####
cluster_options = np.unique(cluster_labels[cluster_labels != -1])
dropdown_cluster = widgets.Dropdown(options=cluster_options, description='Cluster:')
print("Which cluster would you like to see the video frames of?")
display(dropdown_cluster)
Which cluster would you like to see the video frames of?
Dropdown(description='Cluster:', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19…
Make sure to run the cell above to choose which cluster to view!
# The input video path should point to the directory containing the mouse mp4 videos titled as: Animal_11_4DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4
input_video_path = "../labelled_videos" # Change this if your directory is somewhere else
cluster = dropdown_cluster.value
show_frames_for_cluster(input_video_path, cluster, dataset, cluster_labels)
The cell belows shows supervised UMAP results where the freezing frames are used as labels.
supervised_embedding_result, supervised_cluster_labels = fit_umap_and_hdbscan(latents_df, min_clusters=10, max_clusters=40, supervised=True)
plot_umap_and_hdbscan_results(supervised_embedding_result, supervised_cluster_labels, dataset)
UMAP Parameter Grid Search: 100%|██████████████████████████████████████████████████████| 18/18 [23:25<00:00, 78.07s/it]
Best UMAP Parameters: {'min_dist': 0.1, 'n_components': 3, 'n_neighbors': 15}
Best HDBSCAN Parameters: {'min_cluster_size': 70, 'min_samples': 30}
Reconstruction error: 0.006834063678979874
Silhouette score: 0.4389575719833374
Amount of clusters: 15
These plots show the data spatially reduced using UMAP:
These plots show the same UMAP data but clustered using HDBSCAN: Number of clusters (excluding noise): 14
The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. The barcodes also show the freezing frames using black markers. The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). - FRC is the percentage of frames within a cluster that are freezing frames. - CFC is the percentage of total freezing frames that belong to a specific cluster.
This plot shows a heatmap of the pairwise distances between cluster means:
The two cells below will allow you to choose a specific cluster and then visualise the video frames belonging to that cluster for you.
#### Run this cell! ####
cluster_options = np.unique(cluster_labels[cluster_labels != -1])
dropdown_cluster = widgets.Dropdown(options=cluster_options, description='Cluster:')
print("Which cluster would you like to see the video frames of?")
display(dropdown_cluster)
Which cluster would you like to see the video frames of?
Dropdown(description='Cluster:', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19…
Make sure to run the cell above to choose which cluster to view!
# The input video path should point to the directory containing the mouse mp4 videos titled as: Animal_11_4DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4
input_video_path = "../labelled_videos" # Change this if your directory is somewhere else
cluster = dropdown_cluster.value
show_frames_for_cluster(input_video_path, cluster, dataset, cluster_labels)
This cell will downsample the data so that GSBS can be (feasibly) fit on it.
step_size = 20 # step_size denotes how many frames to skip over in order to downsample
downsampled_data, time_series = downsample_data(dataset, step_size)
print(f"Downsampled to {len(time_series)} frames.")
Downsampled to 564 frames.
corr = np.corrcoef(time_series)
plt.imshow(corr)
plt.show()
GSBS_model = GSBS(x=time_series, kmax=int(len(time_series) * 0.5), statewise_detection=True)
GSBS_model.fit()
100%|████████████████████████████████████████████████████████████████████████████████| 281/281 [02:39<00:00, 1.77it/s]
plot_gsbs_results(GSBS_model, time_series)
This plot shows the correlation matrix of the data with the segmented states overlaid in red.
These plots show the measure of fit (left) and the state transitions over time (right). Note that we purposely use the best fit after a minimum of 10 boundaries in order to ensure we have some meaningful states.
This plot shows a histogram of pattern correlations:
The following cell reduces the amount of different states. \ It does this by comparing the states using their correlation and merging states that have a correlation higher than the given threshold.
# You can alter the threshold value to change the resulting number of unique states
merged_states = merge_similar_states(GSBS_model, threshold=0.85)
Reduced the number of unique states from 66 to 14
The next function will print the start and ending frame for all sequences for each cluster. \ This function also 'upsamples' the data using the labelled downsampled data. \ It takes the state labels from the downsampled data and inserts them into the original sized dataset. It then fills in the missing values based on the nearest state label.
state_data, state_labels, labelled_dataset = print_frames_per_state(downsampled_data, dataset, GSBS_model, merged_states)
Sequences for state '6.0': Start frame: 0, end frame: 150 Start frame: 1511, end frame: 1670 Start frame: 2111, end frame: 2330 Start frame: 2871, end frame: 3050 Start frame: 3571, end frame: 3690 Start frame: 4111, end frame: 4330 Start frame: 5251, end frame: 5710 Start frame: 7791, end frame: 8290 Start frame: 8411, end frame: 8770 Start frame: 9711, end frame: 9750 Sequences for state '1.0': Start frame: 151, end frame: 190 Start frame: 611, end frame: 750 Start frame: 1671, end frame: 1930 Start frame: 2511, end frame: 2570 Start frame: 3691, end frame: 3890 Start frame: 5911, end frame: 6570 Start frame: 6671, end frame: 6790 Start frame: 7071, end frame: 7150 Start frame: 7391, end frame: 7470 Start frame: 8291, end frame: 8410 Sequences for state '12.0': Start frame: 191, end frame: 410 Start frame: 511, end frame: 610 Start frame: 891, end frame: 990 Start frame: 1051, end frame: 1290 Start frame: 1991, end frame: 2110 Start frame: 2651, end frame: 2870 Start frame: 3311, end frame: 3410 Start frame: 4551, end frame: 4750 Start frame: 5031, end frame: 5250 Start frame: 7011, end frame: 7070 Start frame: 7151, end frame: 7390 Start frame: 7471, end frame: 7530 Start frame: 7651, end frame: 7790 Start frame: 9391, end frame: 9590 Start frame: 9911, end frame: 10230 Sequences for state '2.0': Start frame: 411, end frame: 510 Start frame: 991, end frame: 1050 Start frame: 1291, end frame: 1510 Start frame: 2331, end frame: 2510 Start frame: 3411, end frame: 3570 Start frame: 4331, end frame: 4550 Start frame: 7531, end frame: 7650 Start frame: 8771, end frame: 9390 Start frame: 9591, end frame: 9710 Start frame: 9751, end frame: 9910 Start frame: 10831, end frame: 10930 Sequences for state '4.0': Start frame: 751, end frame: 890 Start frame: 4751, end frame: 4910 Start frame: 6891, end frame: 7010 Sequences for state '13.0': Start frame: 1931, end frame: 1990 Start frame: 4911, end frame: 5030 Start frame: 10331, end frame: 10830 Start frame: 11051, end frame: 11210 Sequences for state '3.0': Start frame: 2571, end frame: 2650 Start frame: 3991, end frame: 4110 Start frame: 6791, end frame: 6890 Start frame: 10231, end frame: 10330 Start frame: 10931, end frame: 11050 Sequences for state '0.0': Start frame: 3051, end frame: 3310 Sequences for state '10.0': Start frame: 3891, end frame: 3930 Sequences for state '8.0': Start frame: 3931, end frame: 3990 Sequences for state '5.0': Start frame: 5711, end frame: 5790 Start frame: 6611, end frame: 6670 Sequences for state '9.0': Start frame: 5791, end frame: 5910 Sequences for state '11.0': Start frame: 6571, end frame: 6610 Sequences for state '7.0': Start frame: 11211, end frame: 11249
The following plots show what the state pattern looks like and also what the duration of the states is like.
plot_barcodes(merged_states, labelled_dataset)
This plot shows states by colour, showing their order and recurrence throughout the data:
This plot shows the states over the frames and includes markers to show the freezing frames:
The two cells below will allow you to choose a specific state and then visualise the video frames belonging to that state for you.
#### Run this cell! ####
state_options = np.unique(state_labels)
dropdown_state = widgets.Dropdown(options=state_options, description='State:')
print("Which state would you like to see the video frames of?")
display(dropdown_state)
Which state would you like to see the video frames of?
Dropdown(description='State:', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), value=0)
Make sure to run the cell above to choose which state to view!
# The input video path should point to the directory containing the mouse mp4 videos titled as: Animal_11_4DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4
input_video_path = "../labelled_videos" # Change this if your directory is somewhere else
state = dropdown_state.value
state_labels = downsampled_data['state_label']
show_frames_for_cluster(input_video_path, state, downsampled_data, state_labels)
The below functions will train an RNN model on the chosen dataset. \ The model will take 25 frames as input sequence, and attempt to predict the next 25 frames. \ The next input sequence is shifted by 25 frames from the last. \ You can view and/or change the model architecture in the code blocks below.
# This cell sets which device to use. If a GPU is available it will choose that, otherwise it will select CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using", device)
Using cuda
# Parameters for training the RNN
dropout_prob = 0.5
epoch_num = 10
hidden_size = 16 # This defines the size of the latent values
layer_num = 2
learning_rate = 1e-3
seq_size = 25 # The amount of frames inputted as one sequence, 25 frames is 1 second of video
pred_window = 25 # The amount of frames (after the input) the model predicts, 25 frames is 1 second of video
shift_size = 25 # By how many frames each new input for the model is shifted
batch_size = 64 # The batch size for the dataloaders
# This function processes the data, splits it into train, test, and validation, normalizes it, and creates DataLoaders
data_loader = preprocess_data(dataset, seq_size, pred_window, shift_size, batch_size, device)
# Get the input and output size of our data
input_size = next(iter(data_loader))[0][:, :, 1:-2].size(-1)
output_size = next(iter(data_loader))[1][:, :, 1:-2].size(-1)
# Create our model with our parameters and data sizes
model = Model(dropout_prob, hidden_size, layer_num, input_size, output_size).to(device)
# Create a loss function and optimizer for the model
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), learning_rate)
print(model)
Model( (gru): GRU(28, 16, num_layers=2, batch_first=True, dropout=0.5) (linear): Linear(in_features=16, out_features=28, bias=True) )
# Set a save location for the model (including filename for the model)
mouse_no = dataset['mouse_no'].unique().item()
save_dir = f"../Models/rnn_{datatype}_data_1l_mouse_nr_{str(mouse_no)}_hidden_size_{str(hidden_size)}.pt"
train_model(model, epoch_num, optimizer, criterion, data_loader, save_dir)
Epoch 0/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 47.62it/s]
Train loss: 1.0290
Epoch 1/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 47.30it/s]
Train loss: 0.9943
Epoch 2/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 48.28it/s]
Train loss: 0.9631
Epoch 3/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 49.65it/s]
Train loss: 0.9338
Epoch 4/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 46.36it/s]
Train loss: 0.9037
Epoch 5/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 50.00it/s]
Train loss: 0.8734
Epoch 6/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 50.00it/s]
Train loss: 0.8441
Epoch 7/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 50.00it/s]
Train loss: 0.8196
Epoch 8/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 54.26it/s]
Train loss: 0.7936
Epoch 9/10: 100%|████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 39.45it/s]
Train loss: 0.7742
The below functions will load in the saved model and run the full dataset through it. \ Instead of the output (predictions for future sequences) it will collect the intermediate layer outputs. \ This is what we call the latent space and where we will hopefully see (after dimensionality reduction and clustering) learned behaviours (or sequences).
# Load the saved model
model.load_state_dict(torch.load(save_dir))
model.eval()
Model( (gru): GRU(28, 16, num_layers=2, batch_first=True, dropout=0.5) (linear): Linear(in_features=16, out_features=28, bias=True) )
# Analyses the model using the test data, and obtains the performance (loss) and latent values for all sequences
latents_df = extract_latents(model, data_loader, criterion)
100%|████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 41.92it/s]
This cell will downsample the (latent) data so that GSBS can be (feasibly) fit on it.
step_size = 20 # step_size denotes how many frames to skip over in order to downsample
downsampled_data, time_series = downsample_data(latents_df, step_size)
print(f"Downsampled to {len(time_series)} frames.")
Downsampled to 561 frames.
corr = np.corrcoef(time_series)
plt.imshow(corr)
plt.show()
GSBS_model = GSBS(x=time_series, kmax=int(len(time_series) * 0.5), statewise_detection=True)
GSBS_model.fit()
100%|████████████████████████████████████████████████████████████████████████████████| 279/279 [02:32<00:00, 1.82it/s]
plot_gsbs_results(GSBS_model, time_series)
This plot shows the correlation matrix of the data with the segmented states overlaid in red.
These plots show the measure of fit (left) and the state transitions over time (right). Note that we purposely use the best fit after a minimum of 10 boundaries in order to ensure we have some meaningful states.
This plot shows a histogram of pattern correlations:
The following cell reduces the amount of different states. \ It does this by comparing the states using their correlation and merging states that have a correlation higher than the given threshold.
# You can alter the threshold value to change the resulting number of unique states
merged_states = merge_similar_states(GSBS_model, threshold=0.97)
Reduced the number of unique states from 51 to 21
The next function will print the start and ending frame for all sequences for each cluster. \ Note that because of downsampling, some frames inbetween sequences might not be mentioned.
state_data, state_labels, labelled_dataset = print_frames_per_state(downsampled_data, dataset, GSBS_model, merged_states)
Sequences for state '6.0': Start frame: 0, end frame: 30 Sequences for state '12.0': Start frame: 31, end frame: 130 Sequences for state '10.0': Start frame: 131, end frame: 210 Start frame: 5351, end frame: 5550 Start frame: 5931, end frame: 6530 Start frame: 7391, end frame: 7470 Sequences for state '1.0': Start frame: 211, end frame: 410 Start frame: 511, end frame: 630 Start frame: 1051, end frame: 1290 Start frame: 1531, end frame: 1670 Start frame: 3311, end frame: 3390 Start frame: 4551, end frame: 4750 Start frame: 4911, end frame: 5250 Start frame: 9391, end frame: 9590 Sequences for state '13.0': Start frame: 411, end frame: 510 Start frame: 1291, end frame: 1530 Start frame: 1671, end frame: 1930 Start frame: 2331, end frame: 2650 Start frame: 3051, end frame: 3310 Start frame: 3391, end frame: 3570 Start frame: 4331, end frame: 4550 Start frame: 7531, end frame: 7650 Start frame: 7811, end frame: 8410 Start frame: 8771, end frame: 9390 Sequences for state '15.0': Start frame: 631, end frame: 890 Start frame: 991, end frame: 1050 Start frame: 9591, end frame: 9910 Sequences for state '0.0': Start frame: 891, end frame: 990 Start frame: 5711, end frame: 5930 Sequences for state '5.0': Start frame: 1931, end frame: 2330 Start frame: 2651, end frame: 3050 Start frame: 3571, end frame: 3690 Start frame: 4091, end frame: 4330 Start frame: 7151, end frame: 7390 Start frame: 7651, end frame: 7810 Start frame: 8411, end frame: 8770 Start frame: 9911, end frame: 10250 Sequences for state '9.0': Start frame: 3691, end frame: 4090 Sequences for state '18.0': Start frame: 4751, end frame: 4910 Sequences for state '19.0': Start frame: 5251, end frame: 5350 Sequences for state '17.0': Start frame: 5551, end frame: 5710 Sequences for state '20.0': Start frame: 6531, end frame: 6590 Sequences for state '4.0': Start frame: 6591, end frame: 6890 Sequences for state '14.0': Start frame: 6891, end frame: 7030 Sequences for state '3.0': Start frame: 7031, end frame: 7150 Sequences for state '2.0': Start frame: 7471, end frame: 7530 Sequences for state '8.0': Start frame: 10251, end frame: 10330 Start frame: 10931, end frame: 11050 Sequences for state '7.0': Start frame: 10331, end frame: 10830 Sequences for state '16.0': Start frame: 10831, end frame: 10930 Sequences for state '11.0': Start frame: 11051, end frame: 11249
The following plots show what the state pattern looks like and also what the duration of the states is like.
plot_barcodes(merged_states, downsampled_data)
This plot shows states by colour, showing their order and recurrence throughout the data:
This plot shows the states over the frames and includes markers to show the freezing frames:
The two cells below will allow you to choose a specific state and then visualise the video frames belonging to that state for you.
#### Run this cell! ####
state_options = np.unique(state_labels)
dropdown_state = widgets.Dropdown(options=state_options, description='State:')
print("Which state would you like to see the video frames of?")
display(dropdown_state)
Which state would you like to see the video frames of?
Dropdown(description='State:', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, …
Make sure to run the cell above to choose which state to view!
# The input video path should point to the directory containing the mouse mp4 videos titled as: Animal_11_4DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4
input_video_path = "../labelled_videos" # Change this if your directory is somewhere else
state = dropdown_state.value
show_frames_for_cluster(input_video_path, state, downsampled_data, state_labels)
plot_hdbscan_bp(dataset, embedding_result, cluster_labels)
The following plots show 10 individual barcodes for the 10 largest clusters, and one large barcode with all clusters shown. The barcodes also show the freezing frames using black markers. The invidiual plot titles show two percentages: Freezing Rate in Cluster (FRC) and Cluster Freezing Contribution (CFC). - FRC is the percentage of frames within a cluster that are freezing frames. - CFC is the percentage of total freezing frames that belong to a specific cluster.
This chord diagram shows all the connections between the different clusters.
These plot shows all transitions between frames (in 3D and 2D).
This transition matrix is another way of viewing the connections between the clusters. The values in the squares show the amount of transitions between the current and target cluster and the total amount of transitions from this current cluster to all clusters (within brackets).
recurring_patterns = plot_gsbs_bp(merged_states, labelled_dataset)
This plot shows states by colour, showing their order and recurrence throughout the data:
This plot shows the states over the frames and includes markers to show the freezing frames:
This chord diagram shows all the connections between the different states
This transition matrix is another way of viewing the connections between the states. The values in the squares show the amount of transitions between the current and target state and the total amount of transitions from this current state to all states (within brackets).
C:\Users\Kieran\AppData\Local\Temp\ipykernel_21900\3453758245.py:16: RuntimeWarning: invalid value encountered in divide transition_matrix_prob = transition_matrix / np.sum(transition_matrix, axis=1, keepdims=True)
This plot shows a state transition graph where the states are the nodes and the transitions are the lines. The transition weight is shown in the thickness of the lines (and the printed values).
Here we'll print all recurring patterns and where they occur in the order of states: Pattern: (6, 1), Occurrences at indices: [0, 11, 24, 51] Pattern: (1, 12), Occurrences at indices: [1, 45, 47] Pattern: (12, 2), Occurrences at indices: [2, 7, 9, 22, 48, 55] Pattern: (2, 12), Occurrences at indices: [3, 8, 30, 49, 54, 58] Pattern: (12, 1), Occurrences at indices: [4, 44, 46] Pattern: (4, 12), Occurrences at indices: [6, 43] Pattern: (2, 6), Occurrences at indices: [10, 23, 56] Pattern: (13, 12), Occurrences at indices: [13, 33] Pattern: (12, 6), Occurrences at indices: [14, 19, 34, 50] Pattern: (6, 2), Occurrences at indices: [15, 29, 53, 57] Pattern: (1, 3), Occurrences at indices: [17, 41] Pattern: (3, 13), Occurrences at indices: [60, 63] Pattern: (1, 12, 2), Occurrences at indices: [1, 47] Pattern: (12, 2, 12), Occurrences at indices: [2, 7, 48] Pattern: (2, 12, 2), Occurrences at indices: [8, 54] Pattern: (12, 2, 6), Occurrences at indices: [9, 22, 55] Pattern: (2, 6, 1), Occurrences at indices: [10, 23] Pattern: (13, 12, 6), Occurrences at indices: [13, 33] Pattern: (6, 2, 12), Occurrences at indices: [29, 53, 57] Pattern: (12, 1, 12), Occurrences at indices: [44, 46] Pattern: (1, 12, 2, 12), Occurrences at indices: [1, 47] Pattern: (2, 12, 2, 6), Occurrences at indices: [8, 54] Pattern: (12, 2, 6, 1), Occurrences at indices: [9, 22]
#### Run this cell! ####
pattern_options = recurring_patterns.keys()
dropdown_pattern = widgets.Dropdown(options=pattern_options, description='Pattern:')
print("Which pattern would you like to see the video frames of?")
display(dropdown_pattern)
Which pattern would you like to see the video frames of?
Dropdown(description='Pattern:', options=((6, 1), (1, 12), (12, 2), (2, 12), (12, 1), (4, 12), (2, 6), (13, 12…
# The input video path should point to the directory containing the mouse mp4 videos titled as: Animal_11_4DLC_resnet50_Freezing_07-2020Sep29shuffle1_100000_filtered_labeled.mp4
input_video_path = "../labelled_videos" # Change this if your directory is somewhere else
pattern = dropdown_pattern.value
show_frames_for_sequence(input_video_path, pattern, labelled_dataset, state_data)